Artigos de revistas sobre o tema "Neural fields equations"
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Veltz, Romain, e Olivier Faugeras. "A Center Manifold Result for Delayed Neural Fields Equations". SIAM Journal on Mathematical Analysis 45, n.º 3 (janeiro de 2013): 1527–62. http://dx.doi.org/10.1137/110856162.
Texto completo da fonteBelhe, Yash, Michaël Gharbi, Matthew Fisher, Iliyan Georgiev, Ravi Ramamoorthi e Tzu-Mao Li. "Discontinuity-Aware 2D Neural Fields". ACM Transactions on Graphics 42, n.º 6 (5 de dezembro de 2023): 1–11. http://dx.doi.org/10.1145/3618379.
Texto completo da fonteNicks, Rachel, Abigail Cocks, Daniele Avitabile, Alan Johnston e Stephen Coombes. "Understanding Sensory Induced Hallucinations: From Neural Fields to Amplitude Equations". SIAM Journal on Applied Dynamical Systems 20, n.º 4 (janeiro de 2021): 1683–714. http://dx.doi.org/10.1137/20m1366885.
Texto completo da fonteVeltz, Romain, e Olivier Faugeras. "ERRATUM: A Center Manifold Result for Delayed Neural Fields Equations". SIAM Journal on Mathematical Analysis 47, n.º 2 (janeiro de 2015): 1665–70. http://dx.doi.org/10.1137/140962279.
Texto completo da fonteBressloff, Paul C., e Zachary P. Kilpatrick. "Nonlinear Langevin Equations for Wandering Patterns in Stochastic Neural Fields". SIAM Journal on Applied Dynamical Systems 14, n.º 1 (janeiro de 2015): 305–34. http://dx.doi.org/10.1137/140990371.
Texto completo da fonteScheinker, Alexander, e Reeju Pokharel. "Physics-constrained 3D convolutional neural networks for electrodynamics". APL Machine Learning 1, n.º 2 (1 de junho de 2023): 026109. http://dx.doi.org/10.1063/5.0132433.
Texto completo da fonteSim, Fabio M., Eka Budiarto e Rusman Rusyadi. "Comparison and Analysis of Neural Solver Methods for Differential Equations in Physical Systems". ELKHA 13, n.º 2 (22 de outubro de 2021): 134. http://dx.doi.org/10.26418/elkha.v13i2.49097.
Texto completo da fonteITOH, MAKOTO, e LEON O. CHUA. "IMAGE PROCESSING AND SELF-ORGANIZING CNN". International Journal of Bifurcation and Chaos 15, n.º 09 (setembro de 2005): 2939–58. http://dx.doi.org/10.1142/s0218127405013794.
Texto completo da fonteWennekers, Thomas. "Dynamic Approximation of Spatiotemporal Receptive Fields in Nonlinear Neural Field Models". Neural Computation 14, n.º 8 (1 de agosto de 2002): 1801–25. http://dx.doi.org/10.1162/089976602760128027.
Texto completo da fonteMentzer, Katherine L., e J. Luc Peterson. "Neural network surrogate models for equations of state". Physics of Plasmas 30, n.º 3 (março de 2023): 032704. http://dx.doi.org/10.1063/5.0126708.
Texto completo da fonteSamia Atallah. "The Numerical Methods of Fractional Differential Equations". مجلة جامعة بني وليد للعلوم الإنسانية والتطبيقية 8, n.º 4 (25 de setembro de 2023): 496–512. http://dx.doi.org/10.58916/jhas.v8i4.44.
Texto completo da fonteChu, Mengyu, Lingjie Liu, Quan Zheng, Erik Franz, Hans-Peter Seidel, Christian Theobalt e Rhaleb Zayer. "Physics informed neural fields for smoke reconstruction with sparse data". ACM Transactions on Graphics 41, n.º 4 (julho de 2022): 1–14. http://dx.doi.org/10.1145/3528223.3530169.
Texto completo da fonteGuo, Yanan, Xiaoqun Cao, Bainian Liu e Mei Gao. "Solving Partial Differential Equations Using Deep Learning and Physical Constraints". Applied Sciences 10, n.º 17 (26 de agosto de 2020): 5917. http://dx.doi.org/10.3390/app10175917.
Texto completo da fonteRaissi, Maziar, Alireza Yazdani e George Em Karniadakis. "Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations". Science 367, n.º 6481 (30 de janeiro de 2020): 1026–30. http://dx.doi.org/10.1126/science.aaw4741.
Texto completo da fonteKwessi, Eddy. "A Consistent Estimator of Nontrivial Stationary Solutions of Dynamic Neural Fields". Stats 4, n.º 1 (13 de fevereiro de 2021): 122–37. http://dx.doi.org/10.3390/stats4010010.
Texto completo da fonteDi Carlo, D., D. Heitz e T. Corpetti. "Post Processing Sparse And Instantaneous 2D Velocity Fields Using Physics-Informed Neural Networks". Proceedings of the International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics 20 (11 de julho de 2022): 1–11. http://dx.doi.org/10.55037/lxlaser.20th.183.
Texto completo da fonteBÄKER, M., T. KALKREUTER, G. MACK e M. SPEH. "NEURAL MULTIGRID METHODS FOR GAUGE THEORIES AND OTHER DISORDERED SYSTEMS". International Journal of Modern Physics C 04, n.º 02 (abril de 1993): 239–47. http://dx.doi.org/10.1142/s0129183193000252.
Texto completo da fontePang, Xue, Jian Wang, Faliang Yin e Jun Yao. "Construction of elliptic stochastic partial differential equations solver in groundwater flow with convolutional neural networks". Journal of Physics: Conference Series 2083, n.º 4 (1 de novembro de 2021): 042064. http://dx.doi.org/10.1088/1742-6596/2083/4/042064.
Texto completo da fonteAqil, Marco, Selen Atasoy, Morten L. Kringelbach e Rikkert Hindriks. "Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome". PLOS Computational Biology 17, n.º 1 (28 de janeiro de 2021): e1008310. http://dx.doi.org/10.1371/journal.pcbi.1008310.
Texto completo da fontePeng, Liangrong, e Liu Hong. "Recent Advances in Conservation–Dissipation Formalism for Irreversible Processes". Entropy 23, n.º 11 (31 de outubro de 2021): 1447. http://dx.doi.org/10.3390/e23111447.
Texto completo da fonteHu, Beichao, e Dwayne McDaniel. "Applying Physics-Informed Neural Networks to Solve Navier–Stokes Equations for Laminar Flow around a Particle". Mathematical and Computational Applications 28, n.º 5 (13 de outubro de 2023): 102. http://dx.doi.org/10.3390/mca28050102.
Texto completo da fonteShinde, Rajwardhan, Onkar Dherange, Rahul Gavhane, Hemant Koul e Nilam Patil. "HANDWRITTEN MATHEMATICAL EQUATION SOLVER". International Journal of Engineering Applied Sciences and Technology 6, n.º 10 (1 de fevereiro de 2022): 146–49. http://dx.doi.org/10.33564/ijeast.2022.v06i10.018.
Texto completo da fonteYang, Zhou, Yuwang Xu, Jionglin Jing, Xuepeng Fu, Bofu Wang, Haojie Ren, Mengmeng Zhang e Tongxiao Sun. "Investigation of Physics-Informed Neural Networks to Reconstruct a Flow Field with High Resolution". Journal of Marine Science and Engineering 11, n.º 11 (25 de outubro de 2023): 2045. http://dx.doi.org/10.3390/jmse11112045.
Texto completo da fonteTa, Hoa, Shi Wen Wong, Nathan McClanahan, Jung-Han Kimn e Kaiqun Fu. "Exploration on Physics-Informed Neural Networks on Partial Differential Equations (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junho de 2023): 16344–45. http://dx.doi.org/10.1609/aaai.v37i13.27032.
Texto completo da fonteLiu, Xiangdong, e Yu Gu. "Study of Pricing of High-Dimensional Financial Derivatives Based on Deep Learning". Mathematics 11, n.º 12 (11 de junho de 2023): 2658. http://dx.doi.org/10.3390/math11122658.
Texto completo da fonteATALAY, VOLKAN, e EROL GELENBE. "PARALLEL ALGORITHM FOR COLOUR TEXTURE GENERATION USING THE RANDOM NEURAL NETWORK MODEL". International Journal of Pattern Recognition and Artificial Intelligence 06, n.º 02n03 (agosto de 1992): 437–46. http://dx.doi.org/10.1142/s0218001492000266.
Texto completo da fonteTouboul, Jonathan. "Mean-field equations for stochastic firing-rate neural fields with delays: Derivation and noise-induced transitions". Physica D: Nonlinear Phenomena 241, n.º 15 (agosto de 2012): 1223–44. http://dx.doi.org/10.1016/j.physd.2012.03.010.
Texto completo da fonteSchaback, Robert, e Holger Wendland. "Kernel techniques: From machine learning to meshless methods". Acta Numerica 15 (maio de 2006): 543–639. http://dx.doi.org/10.1017/s0962492906270016.
Texto completo da fonteWilliams, Kyle, Stephen Rudin, Daniel Bednarek, Ammad Baig, Adnan Hussain Siddiqui, Elad I. Levy e Ciprian Ionita. "226 Advancing Neurovascular Diagnostics for Abnormal Hemodynamic Conditions Through AI-Driven Physics-informed Neural Networks". Neurosurgery 70, Supplement_1 (abril de 2024): 61. http://dx.doi.org/10.1227/neu.0000000000002809_226.
Texto completo da fonteATALAY, VOLKAN, EROL GELENBE e NESE YALABIK. "THE RANDOM NEURAL NETWORK MODEL FOR TEXTURE GENERATION". International Journal of Pattern Recognition and Artificial Intelligence 06, n.º 01 (abril de 1992): 131–41. http://dx.doi.org/10.1142/s0218001492000072.
Texto completo da fonteBaazeem, Amani S., Muhammad Shoaib Arif e Kamaleldin Abodayeh. "An Efficient and Accurate Approach to Electrical Boundary Layer Nanofluid Flow Simulation: A Use of Artificial Intelligence". Processes 11, n.º 9 (13 de setembro de 2023): 2736. http://dx.doi.org/10.3390/pr11092736.
Texto completo da fonteAra, Asmat, Oyoon Abdul Razzaq e Najeeb Alam Khan. "A Single Layer Functional Link Artificial Neural Network based on Chebyshev Polynomials for Neural Evaluations of Nonlinear Nth Order Fuzzy Differential Equations". Annals of West University of Timisoara - Mathematics and Computer Science 56, n.º 1 (1 de julho de 2018): 3–22. http://dx.doi.org/10.2478/awutm-2018-0001.
Texto completo da fonteChen, Simin, Zhixiang Liu, Wenbo Zhang e Jinkun Yang. "A Hard-Constraint Wide-Body Physics-Informed Neural Network Model for Solving Multiple Cases in Forward Problems for Partial Differential Equations". Applied Sciences 14, n.º 1 (25 de dezembro de 2023): 189. http://dx.doi.org/10.3390/app14010189.
Texto completo da fonteJakeer, Shaik, Seethi Reddy Reddisekhar Reddy, Sathishkumar Veerappampalayam Easwaramoorthy, Hayath Thameem Basha e Jaehyuk Cho. "Exploring the Influence of Induced Magnetic Fields and Double-Diffusive Convection on Carreau Nanofluid Flow through Diverse Geometries: A Comparative Study Using Numerical and ANN Approaches". Mathematics 11, n.º 17 (27 de agosto de 2023): 3687. http://dx.doi.org/10.3390/math11173687.
Texto completo da fontePioch, Fabian, Jan Hauke Harmening, Andreas Maximilian Müller, Franz-Josef Peitzmann, Dieter Schramm e Ould el Moctar. "Turbulence Modeling for Physics-Informed Neural Networks: Comparison of Different RANS Models for the Backward-Facing Step Flow". Fluids 8, n.º 2 (26 de janeiro de 2023): 43. http://dx.doi.org/10.3390/fluids8020043.
Texto completo da fontePortal-Porras, Koldo, Unai Fernandez-Gamiz, Ainara Ugarte-Anero, Ekaitz Zulueta e Asier Zulueta. "Alternative Artificial Neural Network Structures for Turbulent Flow Velocity Field Prediction". Mathematics 9, n.º 16 (14 de agosto de 2021): 1939. http://dx.doi.org/10.3390/math9161939.
Texto completo da fonteAbudusaimaiti, Mairemunisa, Abuduwali Abudukeremu e Amina Sabir. "Fixed/Preassigned-Time Stochastic Synchronization of Complex-Valued Fuzzy Neural Networks with Time Delay". Mathematics 11, n.º 17 (2 de setembro de 2023): 3769. http://dx.doi.org/10.3390/math11173769.
Texto completo da fonteDu, Mengxuan. "Analysis of Chaos Fluctuations in Atmospheric Prediction, Fluid Mechanics and Power System Load Forecasting". Highlights in Science, Engineering and Technology 72 (15 de dezembro de 2023): 594–601. http://dx.doi.org/10.54097/3kqd5952.
Texto completo da fonteHu, Fujia, Weebeng Tay, Yilun Zhou e Boocheong Khoo. "A Novel Hybrid Deep Learning Method for Predicting the Flow Fields of Biomimetic Flapping Wings". Biomimetics 9, n.º 2 (25 de janeiro de 2024): 72. http://dx.doi.org/10.3390/biomimetics9020072.
Texto completo da fonteJenison, Rick L., Richard A. Reale, Joseph E. Hind e John F. Brugge. "Modeling of Auditory Spatial Receptive Fields With Spherical Approximation Functions". Journal of Neurophysiology 80, n.º 5 (1 de novembro de 1998): 2645–56. http://dx.doi.org/10.1152/jn.1998.80.5.2645.
Texto completo da fonteChampion, Kathleen, Bethany Lusch, J. Nathan Kutz e Steven L. Brunton. "Data-driven discovery of coordinates and governing equations". Proceedings of the National Academy of Sciences 116, n.º 45 (21 de outubro de 2019): 22445–51. http://dx.doi.org/10.1073/pnas.1906995116.
Texto completo da fonteZancanaro, Matteo, Markus Mrosek, Giovanni Stabile, Carsten Othmer e Gianluigi Rozza. "Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters". Fluids 6, n.º 8 (22 de agosto de 2021): 296. http://dx.doi.org/10.3390/fluids6080296.
Texto completo da fonteBohner, Martin, Giuseppe Caristi, Shapour Heidarkhani e Shahin Moradi. "Three solutions for a discrete fourth-order boundary value problem with four parameters". Boletim da Sociedade Paranaense de Matemática 42 (19 de abril de 2024): 1–13. http://dx.doi.org/10.5269/bspm.64229.
Texto completo da fonteTodorova, Sonia, e Valérie Ventura. "Neural Decoding: A Predictive Viewpoint". Neural Computation 29, n.º 12 (dezembro de 2017): 3290–310. http://dx.doi.org/10.1162/neco_a_01020.
Texto completo da fonteda Silva, Severino Horácio. "Lower Semicontinuity of Global Attractors for a Class of Evolution Equations of Neural Fields Type in a Bounded Domain". Differential Equations and Dynamical Systems 26, n.º 4 (7 de agosto de 2015): 371–91. http://dx.doi.org/10.1007/s12591-015-0258-6.
Texto completo da fonteGajamannage, K., D. I. Jayathilake, Y. Park e E. M. Bollt. "Recurrent neural networks for dynamical systems: Applications to ordinary differential equations, collective motion, and hydrological modeling". Chaos: An Interdisciplinary Journal of Nonlinear Science 33, n.º 1 (janeiro de 2023): 013109. http://dx.doi.org/10.1063/5.0088748.
Texto completo da fonteSitte, Michael Philip, e Nguyen Anh Khoa Doan. "Velocity reconstruction in puffing pool fires with physics-informed neural networks". Physics of Fluids 34, n.º 8 (agosto de 2022): 087124. http://dx.doi.org/10.1063/5.0097496.
Texto completo da fonteYan, Xiaohui, Fu Du, Tianqi Zhang, Qian Cui, Zuhao Zhu e Ziming Song. "Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network". Water 16, n.º 3 (28 de janeiro de 2024): 425. http://dx.doi.org/10.3390/w16030425.
Texto completo da fonteHu, Yaowei, Yongkai Wu, Lu Zhang e Xintao Wu. "A Generative Adversarial Framework for Bounding Confounded Causal Effects". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 13 (18 de maio de 2021): 12104–12. http://dx.doi.org/10.1609/aaai.v35i13.17437.
Texto completo da fontePeng, Jiang-Zhou, Xianglei Liu, Zhen-Dong Xia, Nadine Aubry, Zhihua Chen e Wei-Tao Wu. "Data-Driven Modeling of Geometry-Adaptive Steady Heat Convection Based on Convolutional Neural Networks". Fluids 6, n.º 12 (1 de dezembro de 2021): 436. http://dx.doi.org/10.3390/fluids6120436.
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