Готові списки джерел за темами / Differentiable simulation

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Добірка наукової літератури з теми "Differentiable simulation"

Автор: Grafiati

Опубліковано: 15 березня 2025

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Статті в журналах з теми "Differentiable simulation"

Stanziola, Antonio, Simon Arridge, Ben Cox, and Bradley Treeby. "Application of differentiable programming to wave simulation." Journal of the Acoustical Society of America 155, no. 3_Supplement (March 1, 2024): A106. http://dx.doi.org/10.1121/10.0026968.

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Wave simulations play a crucial role in a wide range of scientific and engineering applications, including seismic imaging, optical design, and acoustic modeling. Here we explore the advantages of differentiable programming in the context of acoustic wave simulation. Differentiable programming enables us to treat wave simulation as a differentiable function, allowing for the automatic computation of gradients with respect to any continuous input parameter. We demonstrate how this approach can be applied to various types of wave simulations, such as Pseudo-spectral time-domain solvers or iterative solvers. Implementing wave simulators via differentiable programming achieves several benefits. First, it enables efficient and accurate sensitivity analysis: This is particularly valuable for optimization and uncertainty quantification tasks. Second, it facilitates the incorporation of wave simulations into machine learning frameworks, enabling the integration of simulation-based models with data-driven approaches. Third, differentiable programming can accelerate the calibration and inversion of wave simulation models, making it easier to match simulated results to observed data. We present practical examples and discuss potential applications in fields such as geophysics and medical imaging. Our findings highlight the potential of this approach to advance the state-of-the-art in wave simulation techniques and their integration into larger computational pipelines.

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Viswanathan, Venkatasubramanian. "(Invited) Multi-Physics Modeling of Electrochemical Interfacial Phenomena." ECS Meeting Abstracts MA2024-02, no. 26 (November 22, 2024): 2100. https://doi.org/10.1149/ma2024-02262100mtgabs.

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In this talk, I will discuss our simulation approach which is based on creating an end-to-end differentiable simulation stack for electrochemical phenomena and corresponding validation from 1nm - 1mm. DFT simulations, paired with molecular dynamics, chained to phase-field simulations which are then integrated through reduced-order models in pseudo-2D simulations, enabling simulation capability of interfacial phenomena in a fully differentiable way. I will discuss these results in the context of solid-state batteries.

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Son, Sanghyun, Yi-Ling Qiao, Jason Sewall, and Ming C. Lin. "Differentiable Hybrid Traffic Simulation." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–10. http://dx.doi.org/10.1145/3550454.3555492.

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We introduce a novel differentiable hybrid traffic simulator , which simulates traffic using a hybrid model of both macroscopic and microscopic models and can be directly integrated into a neural network for traffic control and flow optimization. This is the first differentiable traffic simulator for macroscopic and hybrid models that can compute gradients for traffic states across time steps and inhomogeneous lanes. To compute the gradient flow between two types of traffic models in a hybrid framework, we present a novel intermediate conversion component that bridges the lanes in a differentiable manner as well. We also show that we can use analytical gradients to accelerate the overall process and enhance scalability. Thanks to these gradients, our simulator can provide more efficient and scalable solutions for complex learning and control problems posed in traffic engineering than other existing algorithms. Refer to https://sites.google.com/umd.edu/diff-hybrid-traffic-sim for our project.

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Wang, Ying, Jasper Verheul, Sang-Hoon Yeo, Nima Khademi Kalantari, and Shinjiro Sueda. "Differentiable Simulation of Inertial Musculotendons." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–11. http://dx.doi.org/10.1145/3550454.3555490.

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We propose a simple and practical approach for incorporating the effects of muscle inertia, which has been ignored by previous musculoskeletal simulators in both graphics and biomechanics. We approximate the inertia of the muscle by assuming that muscle mass is distributed along the centerline of the muscle. We express the motion of the musculotendons in terms of the motion of the skeletal joints using a chain of Jacobians, so that at the top level, only the reduced degrees of freedom of the skeleton are used to completely drive both bones and musculotendons. Our approach can handle all commonly used musculotendon path types, including those with multiple path points and wrapping surfaces. For muscle paths involving wrapping surfaces, we use neural networks to model the Jacobians, trained using existing wrapping surface libraries, which allows us to effectively handle the Jacobian discontinuities that occur when musculotendon paths collide with wrapping surfaces. We demonstrate support for higher-order time integrators, complex joints, inverse dynamics, Hill-type muscle models, and differentiability. In the limit, as the muscle mass is reduced to zero, our approach gracefully degrades to traditional simulators without support for muscle inertia. Finally, it is possible to mix and match inertial and non-inertial musculotendons, depending on the application.

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Schoenholz, Samuel S., and Ekin D. Cubuk. "JAX, M.D. A framework for differentiable physics*." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 12 (December 1, 2021): 124016. http://dx.doi.org/10.1088/1742-5468/ac3ae9.

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Abstract We introduce JAX MD, a software package for performing differentiable physics simulations with a focus on molecular dynamics. JAX MD includes a number of physics simulation environments, as well as interaction potentials and neural networks that can be integrated into these environments without writing any additional code. Since the simulations themselves are differentiable functions, entire trajectories can be differentiated to perform meta-optimization. These features are built on primitive operations, such as spatial partitioning, that allow simulations to scale to hundreds-of-thousands of particles on a single GPU. These primitives are flexible enough that they can be used to scale up workloads outside of molecular dynamics. We present several examples that highlight the features of JAX MD including: integration of graph neural networks into traditional simulations, meta-optimization through minimization of particle packings, and a multi-agent flocking simulation. JAX MD is available at https://www.github.com/google/jax-md.

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Le Lidec, Quentin, Igor Kalevatykh, Ivan Laptev, Cordelia Schmid, and Justin Carpentier. "Differentiable Simulation for Physical System Identification." IEEE Robotics and Automation Letters 6, no. 2 (April 2021): 3413–20. http://dx.doi.org/10.1109/lra.2021.3062323.

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Li 李, Yin 寅., Chirag Modi, Drew Jamieson, Yucheng 宇澄 Zhang 张, Libin 利彬 Lu 陆, Yu 雨. Feng 冯, François Lanusse, and Leslie Greengard. "Differentiable Cosmological Simulation with the Adjoint Method." Astrophysical Journal Supplement Series 270, no. 2 (February 1, 2024): 36. http://dx.doi.org/10.3847/1538-4365/ad0ce7.

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Abstract Rapid advances in deep learning have brought not only a myriad of powerful neural networks, but also breakthroughs that benefit established scientific research. In particular, automatic differentiation (AD) tools and computational accelerators like GPUs have facilitated forward modeling of the Universe with differentiable simulations. Based on analytic or automatic backpropagation, current differentiable cosmological simulations are limited by memory, and thus are subject to a trade-off between time and space/mass resolution, usually sacrificing both. We present a new approach free of such constraints, using the adjoint method and reverse time integration. It enables larger and more accurate forward modeling at the field level, and will improve gradient-based optimization and inference. We implement it in an open-source particle-mesh (PM) N-body library pmwd (PM with derivatives). Based on the powerful AD system JAX, pmwd is fully differentiable, and is highly performant on GPUs.

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Su, Haozhe, Xuan Li, Tao Xue, Chenfanfu Jiang, and Mridul Aanjaneya. "A Generalized Constitutive Model for Versatile MPM Simulation and Inverse Learning with Differentiable Physics." Proceedings of the ACM on Computer Graphics and Interactive Techniques 6, no. 3 (August 16, 2023): 1–20. http://dx.doi.org/10.1145/3606925.

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We present a generalized constitutive model for versatile physics simulation of inviscid fluids, Newtonian viscosity, hyperelasticity, viscoplasticity, elastoplasticity, and other physical effects that arise due to a mixture of these behaviors. The key ideas behind our formulation are the design of a generalized Kirchhoff stress tensor that can describe hyperelasticity, Newtonian viscosity and inviscid fluids, and the use of pre-projection and post-correction rules for simulating material behaviors that involve plasticity, including elastoplasticity and viscoplasticity. We show how our generalized Kirchhoff stress tensor can be coupled together into a generalized constitutive model that allows the simulation of diverse material behaviors by only changing parameter values. We present several side-by-side comparisons with physics simulations for specific constitutive models to show that our generalized model produces visually similar results. More notably, our formulation allows for inverse learning of unknown material properties directly from data using differentiable physics simulations. We present several 3D simulations to highlight the robustness of our method, even with multiple different materials. To the best of our knowledge, our approach is the first to recover the knowledge of unknown material properties without making explicit assumptions about the data.

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Stuyck, Tuur, and Hsiao-yu Chen. "DiffXPBD." Proceedings of the ACM on Computer Graphics and Interactive Techniques 6, no. 3 (August 16, 2023): 1–14. http://dx.doi.org/10.1145/3606923.

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We present DiffXPBD, a novel and efficient analytical formulation for the differentiable position-based simulation of compliant constrained dynamics (XPBD). Our proposed method allows computation of gradients of numerous parameters with respect to a goal function simultaneously leveraging a performant simulation model. The method is efficient, thus enabling differentiable simulations of high resolution geometries and degrees of freedom (DoFs). Collisions are naturally included in the framework. Our differentiable model allows a user to easily add additional optimization variables. Every control variable gradient requires the computation of only a few partial derivatives which can be computed using automatic differentiation code. We demonstrate the efficacy of the method with examples such as elastic cloth and volumetric material parameter estimation, initial value optimization, optimizing for underlying body shape and pose by only observing the clothing, and optimizing a time-varying external force sequence to match sparse keyframe shapes at specific times. Our approach demonstrates excellent efficiency and we demonstrate this on high resolution meshes with optimizations involving over 26 million degrees of freedom. Making an existing solver differentiable requires only a few modifications and the model is compatible with both modern CPU and GPU multi-core hardware.

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Numerow, Logan, Yue Li, Stelian Coros, and Bernhard Thomaszewski. "Differentiable Voronoi Diagrams for Simulation of Cell-Based Mechanical Systems." ACM Transactions on Graphics 43, no. 4 (July 19, 2024): 1–11. http://dx.doi.org/10.1145/3658152.

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Navigating topological transitions in cellular mechanical systems is a significant challenge for existing simulation methods. While abstract models lack predictive capabilities at the cellular level, explicit network representations struggle with topology changes, and per-cell representations are computationally too demanding for large-scale simulations. To address these challenges, we propose a novel cell-centered approach based on differentiable Voronoi diagrams. Representing each cell with a Voronoi site, our method defines shape and topology of the interface network implicitly. In this way, we substantially reduce the number of problem variables, eliminate the need for explicit contact handling, and ensure continuous geometry changes during topological transitions. Closed-form derivatives of network positions facilitate simulation with Newton-type methods for a wide range of per-cell energies. Finally, we extend our differentiable Voronoi diagrams to enable coupling with arbitrary rigid and deformable boundaries. We apply our approach to a diverse set of examples, highlighting splitting and merging of cells as well as neighborhood changes. We illustrate applications to inverse problems by matching soap foam simulations to real-world images. Comparative analysis with explicit cell models reveals that our method achieves qualitatively comparable results at significantly faster computation times.

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Дисертації з теми "Differentiable simulation"

Collins, Jack T. "Simulation to reality and back: A robot's guide to crossing the reality gap." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/230537/1/Jack_Collins_Thesis.pdf.

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Simulation is an indispensable technology within robotics; however, the reality gap prevents many simulated solutions from transferring perfectly to reality. This thesis investigates the reality gap within the context of robotic manipulation. We present studies that first quantify and then benchmark the reality gap when comparing popular robotic simulators to a real-world ground truth collected using motion capture. We then present a promising new method for overcoming the reality gap that employs an online sim-to-real approach that utilises differentiable physics to iteratively narrow the gap and improve the simulation environment using data collected from the real system.

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Yu, Boyang. "High-quality recovery of garment models and sewing patterns from 3D clothed human data." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD056.

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3D peut améliorer l’interprétation des vêtements réels et leur reproduction numérique, avec des applications en RV et essayages virtuels. Cette thèse aborde la reconstruction de vêtements en estimant une réplique animable et son patron 2D. Basée sur un simulateur différentiable, notre approche optimise la forme simulée pour qu’elle corresponde à la cible, tout en préservant des propriétés clés comme la symétrie. Notre pipeline s’aligne sur les standards de l’industrie, ajustant patrons et matériaux pour affiner la géométrie et permettre la réanimation. Nous introduisons également une optimisation par déformation pour raffiner davantage la géométrie du maillage afin de capturer des détails fins, améliorant l’ajustement et facilitant l’enregistrement non rigide. Nos expériences sur données réelles et synthétiques montrent que nos méthodes surpassent l’état de l’art en term de précision
Recovering high-quality garment models from 3D clothed human shapes can enhance the interpretability of real garments and their digital reproduction, benefiting applications like VR and virtual try-ons. This thesis tackles the challenge of reconstructing garment geometry by estimating an animatable replica and its 2D pattern. Using a differentiable cloth simulator, we optimize the simulated garment to match the target shape while preserving key properties like symmetry. Our inverse garment design pipeline aligns with industry-standard modeling and simulation processes, adjusting 2D patterns and material properties to refine geometry and enable reanimation. Additionally, we introduce a deformation-based optimization method that refines mesh geometry to capture fine-grained details, improving fit and supporting non-rigid registration. Experiments on real and synthetic data demonstrate that our methods surpass state-of-the-art techniques in garment model quality and pattern accuracy

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Carli, Henrique. "Sistemas complexos, séries temporais e previsibilidade." Universidade do Estado do Rio de Janeiro, 2011. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=3296.

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Анотація:

Para qualquer sistema observado, físico ou qualquer outro, geralmente se deseja fazer predições para sua evolução futura. Algumas vezes, muito pouco é conhecido sobre o sistema. Se uma série temporal é a única fonte de informação no sistema, predições de valores futuros da série requer uma modelagem da lei da dinâmica do sistema, talvez não linear. Um interesse em particular são as capacidades de previsão do modelo global para análises de séries temporais. Isso pode ser um procedimento muito complexo e computacionalmente muito alto. Nesta dissertação, nos concetraremos em um determinado caso: Em algumas situações, a única informação que se tem sobre o sistema é uma série sequencial de dados (ou série temporal). Supondo que, por detrás de tais dados, exista uma dinâmica de baixa dimensionalidade, existem técnicas para a reconstrução desta dinâmica.O que se busca é desenvolver novas técnicas para poder melhorar o poder de previsão das técnicas já existentes, através da programação computacional em Maple e C/C++.

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Xu, Lina. "Simulation methods for stochastic differential equations in finance." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/134388/1/Lina_Xu_Thesis.pdf.

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This thesis resolves a number of econometric problems relating to the use of stochastic differential equations based on computer-intensive simulation methods. Stochastic differential equations play an important role in modern finance. They have been used to model the trajectories of key variables such as short-term interest rates and the volatility of financial assets. The central theme of the thesis is the use of Hermite polynomials to approximate the transitional probability distribution functions of stochastic differential equations. Based on these approximations, a new method is proposed for simulating solutions to these equations and new testing procedures are developed to examine the fit of the equations to observed data.

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Chalmers, Graeme D. "Implicit numerical simulation of stochastic differential equations with jumps." Thesis, University of Strathclyde, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501657.

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Implicit numerical methods such as the stochastic theta-method offer a practical way to approximate solutions of stochastic differential equations. The method involves a parameter, θ, which is freely chosen. In this thesis, we investigate strong convergence and linear stability, both mean-square and asymptotic, arising from the implementation of the theta-method when applied to ordinary stochastic differential equations incoroporating jumps. Such models are used in several disciplines; in particular, we note their use as models for various financial quantities such as asset prices, interest rates and volatility.

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See, Chong Wee Simon. "Numerical methods for the simulation of dynamic discontinuous systems." Thesis, University of Salford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358276.

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Bächle, Simone. "Numerical solution of differential-algebraic systems arising in circuit simulation." [S.l.] : [s.n.], 2007. http://opus.kobv.de/tuberlin/volltexte/2007/1524.

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Elerian, Ola. "Simulation estimation of continuous-time models with applications to finance." Thesis, University of Oxford, 1999. https://ora.ox.ac.uk/objects/uuid:9538382d-5524-416a-8a95-1b820dd795e1.

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Over recent years, we have witnessed a rapid development in the body of economic theory with applications to finance. It has had great success in finding theoretical explanations to economic phenomena. Typically, theories are employed that are defined by mathematical models. Finance in particular has drawn upon and developed the theory of stochastic differential equations. These produce elegant and tractable frameworks which help us to better understand the world. To directly apply such theories, the models must be assessed and their parameters estimated. Implementation requires the estimation of the model's elements using statistical techniques. These fit the model to the observed data. Unfortunately, existing statistical methods do not work satisfactorily when applied to many financial models. These methods, when applied to complex models often yield inaccurate results. Consequently, simpler analytical models are often preferred, but these are typically unrealistic representations of the underlying process, given the stylised facts reported in the literature. In practical applications, data is observed at discrete intervals and a discretisation is typically used to approximate the continuous-time model. This can lead to biased estimates, since the true underlying model is assumed continuous. This thesis develops new methods to estimate these types of models, with the objective of obtaining more accurate estimates of the underlying parameters present. The methods are applicable to general models. As the solution to the true continuous process is rarely known for these applications, the methods developed rely on building an Euler-Maruyama approximate model and using simulation techniques to obtain the distribution of the unknown quantities of interest. We propose to simulate the missing paths between the observed data points to reduce the bias from the approximate model. Alternatively, one could use a more sophisticated scheme to discretise the process. Unfortunately, their implementation with simulation methods require us to simulate from the density and evaluate the density at any given point. This has until now only been possible for the Euler-Maruyama scheme. One contribution of the thesis is to show the existence of a closed form solution from use of the higher order Milstein scheme. The likelihood based method is implemented within the Bayesian paradigm, as in the context of these models, Bayesian methods are often analytically easier. Concerning the estimation methodology, emphasis is placed on simulation efficiency; design and implementation of the method directly affects the accuracy and stability of the results. In conjunction with estimation, it is important to provide inference and diagnostic procedures. Meaningful information from simulation results must be extracted and summarised. This necessitates developing techniques to evaluate the plausibility and hence the fit of a particular model for a given dataset. An important aspect of model evaluation concerns the ability to compare model fit across a range of possible alternatives. The advantage with the Bayesian framework is that it allows comparison across non-nested models. The aim of the thesis is thus to provide an efficient estimation method for these continuous-time models, that can be used to conduct meaningful inference, with their performance being assessed through the use of diagnostic tools.

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Schwarzenberger, Michael. "Affine Processes and Pseudo-Differential Operators with Unbounded Coefficients." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-211510.

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The concept of pseudo-differential operators allows one to study stochastic processes through their symbol. This approach has generated many new insights in recent years. However, most results are based on the assumption of bounded coefficients. In this thesis, we study Levy-type processes with unbounded coefficients and, especially, affine processes. In particular, we establish a connection between pseudo-differential operators and affine processes which are well-known from mathematical finance. Affine processes are an interesting example in this field since they have linearly growing and hence unbounded coefficients. New techniques and tools are developed to handle the affine case and then expanded to general Levy-type processes. In this way, the convergence of a simulation scheme based on a Markov chain approximation, results on path properties, and necessary conditions for the symmetry of operators were proven.

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Geurts, Kevin Richard. "Stochastic simulation of non-Newtonian flow fields /." Thesis, Connect to this title online; UW restricted, 1995. http://hdl.handle.net/1773/9821.

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Книги з теми "Differentiable simulation"

Tiberiu, Coloși, ed. Numerical modeling and simulation of dynamical systems. Cluj-Napoca, Romania: Casa Cărṭii de Ştiinṭă, 1995.

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Petráš, Ivo. Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Fiedler, Bernold. Ergodic Theory, Analysis, and Efficient Simulation of Dynamical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001.

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Campbell, S. L. Modeling and simulation in Scilab/Scicos with ScicosLab 4.4. 2nd ed. New York: Springer, 2010.

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Korn, Granino Arthur. Numerical insights into dynamic systems: Interactive dynamic system simulation with Microsoft Windows 95 and NT. Amsterdam: Gordon and Breach Science Publishers, 1998.

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International Conference "Dynamical Systems - Theory and Applications" (9th 2007 Łódź, Poland). Modeling, simulation and control of nonlinear engineering dynamical systems: State-of-the-art, perspectives and applications. Edited by Awrejcewicz J. [S.l.]: Springer, 2009.

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Iacus, Stefano M. Simulation and Inference for Stochastic Differential Equations. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-75839-8.

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(Pekka), Neittaanmäki P., and SpringerLink (Online service), eds. Partial Differential Equations: Modeling and Numerical Simulation. Dordrecht: Springer Science + Business Media B.V., 2008.

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Iacus, Stefano M. Simulation and inference for stochastic differential equations: With r examples. New York, N. Y: Springer, 2008.

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W, Campbell Scott, ed. A first course in differential equations, modeling, and simulation. Boca Raton, FL: CRC Press, 2011.

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Частини книг з теми "Differentiable simulation"

Roy, Ankit. "Simulation of Lotka–Volterra Equations Using Differentiable Programming in Julia." In Data Management, Analytics and Innovation, 3–9. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2937-2_1.

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Martinot, Sonia, Nikos Komodakis, Maria Vakalopoulou, Norbert Bus, Charlotte Robert, Eric Deutsch, and Nikos Paragios. "Differentiable Gamma Index-Based Loss Functions: Accelerating Monte-Carlo Radiotherapy Dose Simulation." In Lecture Notes in Computer Science, 485–96. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34048-2_37.

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Britz, Dieter. "Ordinary Differential Equations." In Digital Simulation in Electrochemistry, 51–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31524-7_4.

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Theorell, Axel, and Jörg Stelling. "Microbial Community Decision Making Models in Batch and Chemostat Cultures." In Computational Methods in Systems Biology, 141–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85633-5_9.

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AbstractMicrobial community simulations using genome scale metabolic networks (GSMs) are relevant for many application areas, such as the analysis of the human microbiome. Such simulations rely on assumptions about the culturing environment, affecting if the culture may reach a metabolically stationary state with constant microbial concentrations. They also require assumptions on decision making by the microbes: metabolic strategies can be in the interest of individual community members or of the whole community. However, the impact of such common assumptions on community simulation results has not been investigated systematically. Here, we investigate four combinations of assumptions, elucidate how they are applied in literature, provide novel mathematical formulations for their simulation, and show how the resulting predictions differ qualitatively. Crucially, our results stress that different assumption combinations give qualitatively different predictions on microbial coexistence by differential substrate utilization. This fundamental mechanism is critically under explored in the steady state GSM literature with its strong focus on coexistence states due to crossfeeding (division of labor).

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Kinser, Jason M. "Coupled Differential Equations." In Modeling and Simulation in Python, 219–40. New York: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003226581-15.

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Barnes, David J., and Dominique Chu. "Differential Equations." In Guide to Simulation and Modeling for Biosciences, 121–74. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6762-4_4.

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Weißenfels, Christian. "Differential Equations." In Simulation of Additive Manufacturing using Meshfree Methods, 19–33. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87337-0_3.

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Pramanick, Achintya Kumar. "Differential Equations of Thermodynamics." In Computing and Simulation for Engineers, 175–98. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003222255-12.

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Zhang, Weijian. "Analytical Analysis of a Stochastic Partial Differential Equation." In Advances in Simulation, 97–104. New York, NY: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-6389-7_17.

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Szczepaniak, P. S., and A. Małolepszy. "On Computational Solution of Differential Equations with Delay." In Advances in Simulation, 183–88. New York, NY: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-6389-7_38.

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Тези доповідей конференцій з теми "Differentiable simulation"

Freivalds, Kārlis, Laura Leja, and Oskars Teikmanis. "Learning to Move Objects With Fluid Streams in a Differentiable Simulation." In 2024 7th Iberian Robotics Conference (ROBOT), 1–7. IEEE, 2024. https://doi.org/10.1109/robot61475.2024.10796911.

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Paillet, L., A. Rouxel, H. Carfantan, A. Monmayrant, and S. Lacroix. "Differentiable chief-ray tracing simulator for coded-aperture spectral imaging." In Computational Optical Sensing and Imaging, CTh4B.2. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cosi.2024.cth4b.2.

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We introduce an end-to-end differentiable simulation framework for designing Coded-Aperture Snapshot Spectral Imagers (CASSI) and exploring acquisition strategies. By leveraging automatic differentiation, we dimension the optical system and optimize the coded aperture, enhancing imaging quality and facilitating effective co-design of the instrument.

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Rabiepoor, Arash, Leslie A. Rusch, and Ming Zeng. "RL-Based Digital Pre-distortion for Drive Signals in Non-Differentiable Channel." In Signal Processing in Photonic Communications, SpTu2H.3. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/sppcom.2024.sptu2h.3.

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This paper proposes a novel digital pre-distortion based on deep reinforcement learning to combat instantaneous nonlinearities in electrical back-to-back communication systems. Simulation results demonstrate its superiority over the case without DPD in terms of bit error rate performance.

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Liu, Min, Gang Yang, Siyuan Luo, and Lin Shao. "SoftMAC: Differentiable Soft Body Simulation with Forecast-based Contact Model and Two-way Coupling with Articulated Rigid Bodies and Clothes." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 12008–15. IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801308.

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Wang, Zixiao, Jieya Zhou, Su Zheng, Shuo Yin, Kaichao Liang, Shoubo Hu, Xiao Chen, and Bei Yu. "TorchResist: open-source differentiable resist simulator." In DTCO and Computational Patterning IV, edited by Neal V. Lafferty and Harsha Grunes, 75. SPIE, 2025. https://doi.org/10.1117/12.3062763.

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Coros, Stelian, Miles Macklin, Bernhard Thomaszewski, and Nils Thürey. "Differentiable simulation." In SA '21: SIGGRAPH Asia 2021. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3476117.3483433.

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Planche, Benjamin, and Rajat Vikram Singh. "Physics-based Differentiable Depth Sensor Simulation." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.01412.

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Heiden, Eric, Christopher E. Denniston, David Millard, Fabio Ramos, and Gaurav S. Sukhatme. "Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation." In 2022 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2022. http://dx.doi.org/10.1109/icra46639.2022.9812293.

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Younis, Rami, and Khalid Aziz. "Parallel Automatically Differentiable Data-Types for Next-Generation Simulator Development." In SPE Reservoir Simulation Symposium. Society of Petroleum Engineers, 2007. http://dx.doi.org/10.2118/106493-ms.

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Gjoka, Arvi, Espen Knoop, Moritz Bächer, Denis Zorin, and Daniele Panozzo. "Soft Pneumatic Actuator Design using Differentiable Simulation." In SIGGRAPH '24: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3641519.3657467.

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Звіти організацій з теми "Differentiable simulation"

Bramwell, J., T. Kolev, and R. Rieben. Differentiable Multiphysics Codes: A Breakthrough Technology for Simulation and Computing. Office of Scientific and Technical Information (OSTI), January 2025. https://doi.org/10.2172/2513976.

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Rivera-Casillas, Peter, and Ian Dettwiller. Neural Ordinary Differential Equations for rotorcraft aerodynamics. Engineer Research and Development Center (U.S.), April 2024. http://dx.doi.org/10.21079/11681/48420.

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High-fidelity computational simulations of aerodynamics and structural dynamics on rotorcraft are essential for helicopter design, testing, and evaluation. These simulations usually entail a high computational cost even with modern high-performance computing resources. Reduced order models can significantly reduce the computational cost of simulating rotor revolutions. However, reduced order models are less accurate than traditional numerical modeling approaches, making them unsuitable for research and design purposes. This study explores the use of a new modified Neural Ordinary Differential Equation (NODE) approach as a machine learning alternative to reduced order models in rotorcraft applications—specifically to predict the pitching moment on a rotor blade section from an initial condition, mach number, chord velocity and normal velocity. The results indicate that NODEs cannot outperform traditional reduced order models, but in some cases they can outperform simple multilayer perceptron networks. Additionally, the mathematical structure provided by NODEs seems to favor time-dependent predictions. We demonstrate how this mathematical structure can be easily modified to tackle more complex problems. The work presented in this report is intended to establish an initial evaluation of the usability of the modified NODE approach for time-dependent modeling of complex dynamics over seen and unseen domains.

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Garrison, J. C. Stochastic differential equations and numerical simulation for pedestrians. Office of Scientific and Technical Information (OSTI), July 1993. http://dx.doi.org/10.2172/10184120.

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Costello, Mark F. Modeling and Simulation of a Differential Roll Projectile. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada382708.

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Meldrum, William R., Francis B. Hoogterp, and Alexander R. Kovnat. Modeling and Simulation of a Differential Torque Steered Wheeled Vehicle. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada408819.

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Goodsell, Alison V., Vladimir Henzl, and Martyn T. Swinhoe. Differential Die-Away Instrument: Report on Initial Simulations of Spent Fuel Experiment. Office of Scientific and Technical Information (OSTI), April 2014. http://dx.doi.org/10.2172/1126685.

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Kuruvilla Verghese. Mammographic Imaging Studies Using the Monte Carlo Image Simulation-Differential Sampling (MCMIS-DS) Code. Office of Scientific and Technical Information (OSTI), April 2002. http://dx.doi.org/10.2172/793508.

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Goodsell, Alison Victoria, Vladimir Henzl, Martyn Thomas Swinhoe, Carlos D. Rael, and David J. Desimone. Differential die-away instrument: Report on comparison of fuel assembly experiments and simulations. Office of Scientific and Technical Information (OSTI), January 2015. http://dx.doi.org/10.2172/1167487.

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Berkooz, Gal. A DOMAIN Specific Library and APE for Simulation of Partial Differential Equations in Heterogeneous Environments. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/adb238737.

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George, Jowers, and Grimley. PR-015-08605-R01 Assessment of Orifice Meter Flow Measurements with Low Differential Pressures - Unblinded. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2009. http://dx.doi.org/10.55274/r0010948.

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Measurement of natural gas flow with an orifice meter is a well-established methodology; however, orifice measurement accuracy is of concern when flow rates are low and the differential pressure (DP) across the orifice is at the extreme low end of common DP transmitter ranges. This research evaluated the performance of multiple types of transmitters in 10-inch and 4-inch orifice meter runs at differential pressures approaching 1 inch of water column (1� H2O), simulating low flow transmission meter stations and depleted production well stations. The results were analyzed to characterize and better understand the uncertainties and measurement errors associated with orifice meters operating with small bore diameters and low DPs. Transmitters tested included typical DP transmitters with stated accuracies of 0.1% of full scale, DP transmitters with stated accuracies as a percent of reading; and high-frequency-response DP transmitters. Data acquisition methods, transmitter technologies, and various calibrated measurement spans were studied for their potential to improve orifice meter accuracy at low DP conditions. This is an un-blinded report.

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