Artykuły w czasopismach na temat „Neural fields equations”
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Veltz, Romain, i Olivier Faugeras. "A Center Manifold Result for Delayed Neural Fields Equations". SIAM Journal on Mathematical Analysis 45, nr 3 (styczeń 2013): 1527–62. http://dx.doi.org/10.1137/110856162.
Pełny tekst źródłaBelhe, Yash, Michaël Gharbi, Matthew Fisher, Iliyan Georgiev, Ravi Ramamoorthi i Tzu-Mao Li. "Discontinuity-Aware 2D Neural Fields". ACM Transactions on Graphics 42, nr 6 (5.12.2023): 1–11. http://dx.doi.org/10.1145/3618379.
Pełny tekst źródłaNicks, Rachel, Abigail Cocks, Daniele Avitabile, Alan Johnston i Stephen Coombes. "Understanding Sensory Induced Hallucinations: From Neural Fields to Amplitude Equations". SIAM Journal on Applied Dynamical Systems 20, nr 4 (styczeń 2021): 1683–714. http://dx.doi.org/10.1137/20m1366885.
Pełny tekst źródłaVeltz, Romain, i Olivier Faugeras. "ERRATUM: A Center Manifold Result for Delayed Neural Fields Equations". SIAM Journal on Mathematical Analysis 47, nr 2 (styczeń 2015): 1665–70. http://dx.doi.org/10.1137/140962279.
Pełny tekst źródłaBressloff, Paul C., i Zachary P. Kilpatrick. "Nonlinear Langevin Equations for Wandering Patterns in Stochastic Neural Fields". SIAM Journal on Applied Dynamical Systems 14, nr 1 (styczeń 2015): 305–34. http://dx.doi.org/10.1137/140990371.
Pełny tekst źródłaScheinker, Alexander, i Reeju Pokharel. "Physics-constrained 3D convolutional neural networks for electrodynamics". APL Machine Learning 1, nr 2 (1.06.2023): 026109. http://dx.doi.org/10.1063/5.0132433.
Pełny tekst źródłaSim, Fabio M., Eka Budiarto i Rusman Rusyadi. "Comparison and Analysis of Neural Solver Methods for Differential Equations in Physical Systems". ELKHA 13, nr 2 (22.10.2021): 134. http://dx.doi.org/10.26418/elkha.v13i2.49097.
Pełny tekst źródłaITOH, MAKOTO, i LEON O. CHUA. "IMAGE PROCESSING AND SELF-ORGANIZING CNN". International Journal of Bifurcation and Chaos 15, nr 09 (wrzesień 2005): 2939–58. http://dx.doi.org/10.1142/s0218127405013794.
Pełny tekst źródłaWennekers, Thomas. "Dynamic Approximation of Spatiotemporal Receptive Fields in Nonlinear Neural Field Models". Neural Computation 14, nr 8 (1.08.2002): 1801–25. http://dx.doi.org/10.1162/089976602760128027.
Pełny tekst źródłaMentzer, Katherine L., i J. Luc Peterson. "Neural network surrogate models for equations of state". Physics of Plasmas 30, nr 3 (marzec 2023): 032704. http://dx.doi.org/10.1063/5.0126708.
Pełny tekst źródłaSamia Atallah. "The Numerical Methods of Fractional Differential Equations". مجلة جامعة بني وليد للعلوم الإنسانية والتطبيقية 8, nr 4 (25.09.2023): 496–512. http://dx.doi.org/10.58916/jhas.v8i4.44.
Pełny tekst źródłaChu, Mengyu, Lingjie Liu, Quan Zheng, Erik Franz, Hans-Peter Seidel, Christian Theobalt i Rhaleb Zayer. "Physics informed neural fields for smoke reconstruction with sparse data". ACM Transactions on Graphics 41, nr 4 (lipiec 2022): 1–14. http://dx.doi.org/10.1145/3528223.3530169.
Pełny tekst źródłaGuo, Yanan, Xiaoqun Cao, Bainian Liu i Mei Gao. "Solving Partial Differential Equations Using Deep Learning and Physical Constraints". Applied Sciences 10, nr 17 (26.08.2020): 5917. http://dx.doi.org/10.3390/app10175917.
Pełny tekst źródłaRaissi, Maziar, Alireza Yazdani i George Em Karniadakis. "Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations". Science 367, nr 6481 (30.01.2020): 1026–30. http://dx.doi.org/10.1126/science.aaw4741.
Pełny tekst źródłaKwessi, Eddy. "A Consistent Estimator of Nontrivial Stationary Solutions of Dynamic Neural Fields". Stats 4, nr 1 (13.02.2021): 122–37. http://dx.doi.org/10.3390/stats4010010.
Pełny tekst źródłaDi Carlo, D., D. Heitz i 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 (11.07.2022): 1–11. http://dx.doi.org/10.55037/lxlaser.20th.183.
Pełny tekst źródłaBÄKER, M., T. KALKREUTER, G. MACK i M. SPEH. "NEURAL MULTIGRID METHODS FOR GAUGE THEORIES AND OTHER DISORDERED SYSTEMS". International Journal of Modern Physics C 04, nr 02 (kwiecień 1993): 239–47. http://dx.doi.org/10.1142/s0129183193000252.
Pełny tekst źródłaPang, Xue, Jian Wang, Faliang Yin i Jun Yao. "Construction of elliptic stochastic partial differential equations solver in groundwater flow with convolutional neural networks". Journal of Physics: Conference Series 2083, nr 4 (1.11.2021): 042064. http://dx.doi.org/10.1088/1742-6596/2083/4/042064.
Pełny tekst źródłaAqil, Marco, Selen Atasoy, Morten L. Kringelbach i Rikkert Hindriks. "Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome". PLOS Computational Biology 17, nr 1 (28.01.2021): e1008310. http://dx.doi.org/10.1371/journal.pcbi.1008310.
Pełny tekst źródłaPeng, Liangrong, i Liu Hong. "Recent Advances in Conservation–Dissipation Formalism for Irreversible Processes". Entropy 23, nr 11 (31.10.2021): 1447. http://dx.doi.org/10.3390/e23111447.
Pełny tekst źródłaHu, Beichao, i Dwayne McDaniel. "Applying Physics-Informed Neural Networks to Solve Navier–Stokes Equations for Laminar Flow around a Particle". Mathematical and Computational Applications 28, nr 5 (13.10.2023): 102. http://dx.doi.org/10.3390/mca28050102.
Pełny tekst źródłaShinde, Rajwardhan, Onkar Dherange, Rahul Gavhane, Hemant Koul i Nilam Patil. "HANDWRITTEN MATHEMATICAL EQUATION SOLVER". International Journal of Engineering Applied Sciences and Technology 6, nr 10 (1.02.2022): 146–49. http://dx.doi.org/10.33564/ijeast.2022.v06i10.018.
Pełny tekst źródłaYang, Zhou, Yuwang Xu, Jionglin Jing, Xuepeng Fu, Bofu Wang, Haojie Ren, Mengmeng Zhang i Tongxiao Sun. "Investigation of Physics-Informed Neural Networks to Reconstruct a Flow Field with High Resolution". Journal of Marine Science and Engineering 11, nr 11 (25.10.2023): 2045. http://dx.doi.org/10.3390/jmse11112045.
Pełny tekst źródłaTa, Hoa, Shi Wen Wong, Nathan McClanahan, Jung-Han Kimn i Kaiqun Fu. "Exploration on Physics-Informed Neural Networks on Partial Differential Equations (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 13 (26.06.2023): 16344–45. http://dx.doi.org/10.1609/aaai.v37i13.27032.
Pełny tekst źródłaLiu, Xiangdong, i Yu Gu. "Study of Pricing of High-Dimensional Financial Derivatives Based on Deep Learning". Mathematics 11, nr 12 (11.06.2023): 2658. http://dx.doi.org/10.3390/math11122658.
Pełny tekst źródłaATALAY, VOLKAN, i EROL GELENBE. "PARALLEL ALGORITHM FOR COLOUR TEXTURE GENERATION USING THE RANDOM NEURAL NETWORK MODEL". International Journal of Pattern Recognition and Artificial Intelligence 06, nr 02n03 (sierpień 1992): 437–46. http://dx.doi.org/10.1142/s0218001492000266.
Pełny tekst źródłaTouboul, Jonathan. "Mean-field equations for stochastic firing-rate neural fields with delays: Derivation and noise-induced transitions". Physica D: Nonlinear Phenomena 241, nr 15 (sierpień 2012): 1223–44. http://dx.doi.org/10.1016/j.physd.2012.03.010.
Pełny tekst źródłaSchaback, Robert, i Holger Wendland. "Kernel techniques: From machine learning to meshless methods". Acta Numerica 15 (maj 2006): 543–639. http://dx.doi.org/10.1017/s0962492906270016.
Pełny tekst źródłaWilliams, Kyle, Stephen Rudin, Daniel Bednarek, Ammad Baig, Adnan Hussain Siddiqui, Elad I. Levy i Ciprian Ionita. "226 Advancing Neurovascular Diagnostics for Abnormal Hemodynamic Conditions Through AI-Driven Physics-informed Neural Networks". Neurosurgery 70, Supplement_1 (kwiecień 2024): 61. http://dx.doi.org/10.1227/neu.0000000000002809_226.
Pełny tekst źródłaATALAY, VOLKAN, EROL GELENBE i NESE YALABIK. "THE RANDOM NEURAL NETWORK MODEL FOR TEXTURE GENERATION". International Journal of Pattern Recognition and Artificial Intelligence 06, nr 01 (kwiecień 1992): 131–41. http://dx.doi.org/10.1142/s0218001492000072.
Pełny tekst źródłaBaazeem, Amani S., Muhammad Shoaib Arif i Kamaleldin Abodayeh. "An Efficient and Accurate Approach to Electrical Boundary Layer Nanofluid Flow Simulation: A Use of Artificial Intelligence". Processes 11, nr 9 (13.09.2023): 2736. http://dx.doi.org/10.3390/pr11092736.
Pełny tekst źródłaAra, Asmat, Oyoon Abdul Razzaq i 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, nr 1 (1.07.2018): 3–22. http://dx.doi.org/10.2478/awutm-2018-0001.
Pełny tekst źródłaChen, Simin, Zhixiang Liu, Wenbo Zhang i 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, nr 1 (25.12.2023): 189. http://dx.doi.org/10.3390/app14010189.
Pełny tekst źródłaJakeer, Shaik, Seethi Reddy Reddisekhar Reddy, Sathishkumar Veerappampalayam Easwaramoorthy, Hayath Thameem Basha i 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, nr 17 (27.08.2023): 3687. http://dx.doi.org/10.3390/math11173687.
Pełny tekst źródłaPioch, Fabian, Jan Hauke Harmening, Andreas Maximilian Müller, Franz-Josef Peitzmann, Dieter Schramm i Ould el Moctar. "Turbulence Modeling for Physics-Informed Neural Networks: Comparison of Different RANS Models for the Backward-Facing Step Flow". Fluids 8, nr 2 (26.01.2023): 43. http://dx.doi.org/10.3390/fluids8020043.
Pełny tekst źródłaPortal-Porras, Koldo, Unai Fernandez-Gamiz, Ainara Ugarte-Anero, Ekaitz Zulueta i Asier Zulueta. "Alternative Artificial Neural Network Structures for Turbulent Flow Velocity Field Prediction". Mathematics 9, nr 16 (14.08.2021): 1939. http://dx.doi.org/10.3390/math9161939.
Pełny tekst źródłaAbudusaimaiti, Mairemunisa, Abuduwali Abudukeremu i Amina Sabir. "Fixed/Preassigned-Time Stochastic Synchronization of Complex-Valued Fuzzy Neural Networks with Time Delay". Mathematics 11, nr 17 (2.09.2023): 3769. http://dx.doi.org/10.3390/math11173769.
Pełny tekst źródłaDu, Mengxuan. "Analysis of Chaos Fluctuations in Atmospheric Prediction, Fluid Mechanics and Power System Load Forecasting". Highlights in Science, Engineering and Technology 72 (15.12.2023): 594–601. http://dx.doi.org/10.54097/3kqd5952.
Pełny tekst źródłaHu, Fujia, Weebeng Tay, Yilun Zhou i Boocheong Khoo. "A Novel Hybrid Deep Learning Method for Predicting the Flow Fields of Biomimetic Flapping Wings". Biomimetics 9, nr 2 (25.01.2024): 72. http://dx.doi.org/10.3390/biomimetics9020072.
Pełny tekst źródłaJenison, Rick L., Richard A. Reale, Joseph E. Hind i John F. Brugge. "Modeling of Auditory Spatial Receptive Fields With Spherical Approximation Functions". Journal of Neurophysiology 80, nr 5 (1.11.1998): 2645–56. http://dx.doi.org/10.1152/jn.1998.80.5.2645.
Pełny tekst źródłaChampion, Kathleen, Bethany Lusch, J. Nathan Kutz i Steven L. Brunton. "Data-driven discovery of coordinates and governing equations". Proceedings of the National Academy of Sciences 116, nr 45 (21.10.2019): 22445–51. http://dx.doi.org/10.1073/pnas.1906995116.
Pełny tekst źródłaZancanaro, Matteo, Markus Mrosek, Giovanni Stabile, Carsten Othmer i Gianluigi Rozza. "Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters". Fluids 6, nr 8 (22.08.2021): 296. http://dx.doi.org/10.3390/fluids6080296.
Pełny tekst źródłaBohner, Martin, Giuseppe Caristi, Shapour Heidarkhani i Shahin Moradi. "Three solutions for a discrete fourth-order boundary value problem with four parameters". Boletim da Sociedade Paranaense de Matemática 42 (19.04.2024): 1–13. http://dx.doi.org/10.5269/bspm.64229.
Pełny tekst źródłaTodorova, Sonia, i Valérie Ventura. "Neural Decoding: A Predictive Viewpoint". Neural Computation 29, nr 12 (grudzień 2017): 3290–310. http://dx.doi.org/10.1162/neco_a_01020.
Pełny tekst źródłada 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, nr 4 (7.08.2015): 371–91. http://dx.doi.org/10.1007/s12591-015-0258-6.
Pełny tekst źródłaGajamannage, K., D. I. Jayathilake, Y. Park i 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, nr 1 (styczeń 2023): 013109. http://dx.doi.org/10.1063/5.0088748.
Pełny tekst źródłaSitte, Michael Philip, i Nguyen Anh Khoa Doan. "Velocity reconstruction in puffing pool fires with physics-informed neural networks". Physics of Fluids 34, nr 8 (sierpień 2022): 087124. http://dx.doi.org/10.1063/5.0097496.
Pełny tekst źródłaYan, Xiaohui, Fu Du, Tianqi Zhang, Qian Cui, Zuhao Zhu i Ziming Song. "Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network". Water 16, nr 3 (28.01.2024): 425. http://dx.doi.org/10.3390/w16030425.
Pełny tekst źródłaHu, Yaowei, Yongkai Wu, Lu Zhang i Xintao Wu. "A Generative Adversarial Framework for Bounding Confounded Causal Effects". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 13 (18.05.2021): 12104–12. http://dx.doi.org/10.1609/aaai.v35i13.17437.
Pełny tekst źródłaPeng, Jiang-Zhou, Xianglei Liu, Zhen-Dong Xia, Nadine Aubry, Zhihua Chen i Wei-Tao Wu. "Data-Driven Modeling of Geometry-Adaptive Steady Heat Convection Based on Convolutional Neural Networks". Fluids 6, nr 12 (1.12.2021): 436. http://dx.doi.org/10.3390/fluids6120436.
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