De Ryck, Tim

1. Convolutional Neural Operators for robust and accurate learning of PDEs
   2023/02/02 by Bogdan Raonić, R. Molinaro, Raonić, Bogdan +10 · 68 citations
   Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Model Reduction and Neural Networks #Neural Networks and Applications
2. Error analysis for physics informed neural networks (PINNs)\n approximating Kolmogorov PDEs
   2021/06/28 by Tim De Ryck, Siddhartha Mishra, De Ryck, Tim +1 · 11 citations
   Physics and Astronomy · Computer Science · #Model Reduction and Neural Networks #Neural Networks and Applications #Gaussian Processes and Bayesian Inference
3. wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws
   2022/07/18 by Tim De Ryck, De Ryck, Tim, Siddhartha Mishra +3 · 9 citations
   Computer Science · Engineering · Physics and Astronomy · #Analysis of PDEs (math.AP) #FOS: Computer and information sciences #FOS: Mathematics #Fluid Dynamics and Turbulent Flows #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Numerical Analysis (math.NA)
4. Error estimates for physics informed neural networks approximating the Navier-Stokes equations
   2022/03/17 by De Ryck, Tim, Jagtap, Ameya D., Mishra, Siddhartha · 8 citations
   #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Numerical Analysis (math.NA)
5. Generic bounds on the approximation error for physics-informed (and) operator learning
   2022/05/23 by Tim De Ryck, De Ryck, Tim, Siddhartha Mishra +1 · 7 citations
   Computer Science · Engineering · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Fluid Dynamics and Turbulent Flows #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Neural Networks and Applications #Numerical Analysis (math.NA)
6. Variable-Input Deep Operator Networks
   2022/05/23 by Prasthofer, Michael, De Ryck, Tim, Mishra, Siddhartha · 5 citations
   #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA)
7. An operator preconditioning perspective on training in physics-informed machine learning
   2023/10/09 by Tim De Ryck, Florent Bonnet, De Ryck, Tim +5 · 2 citations
   Physics and Astronomy · Engineering · Decision Sciences · #Model Reduction and Neural Networks #Nuclear reactor physics and engineering #Probabilistic and Robust Engineering Design
8. On the approximation of rough functions with deep neural networks
   2019/12/13 by De Ryck, Tim, Mishra, Siddhartha, Ray, Deep · 1 citation
   #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA)