A Differentiable Programming System to Bridge Machine Learning and Scientific Computing

17 July 2019

Papers citing "A Differentiable Programming System to Bridge Machine Learning and Scientific Computing"

32 / 32 papers shown

Title
Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Abdolmehdi Behroozi Chaopeng Shen and Daniel Kifer AI4CE 30 0 0 13 May 2025
A Common Interface for Automatic Differentiation Guillaume Dalle Adrian Hill PINN VLM 52 0 0 08 May 2025
InvertibleNetworks.jl: A Julia package for scalable normalizing flows Rafael Orozco Philipp A. Witte M. Louboutin Ali Siahkoohi G. Rizzuti Bas Peters Felix J. Herrmann 38 9 0 20 Dec 2023
Differentiable Visual Computing for Inverse Problems and Machine Learning Andrew Spielberg Fangcheng Zhong Konstantinos Rematas Krishna Murthy Jatavallabhula Cengiz Öztireli Tzu-Mao Li Derek Nowrouzezahrai 51 7 0 21 Nov 2023
Randomized Forward Mode of Automatic Differentiation For Optimization Algorithms Khemraj Shukla Yeonjong Shin ODL 26 4 0 22 Oct 2023
Learned multiphysics inversion with differentiable programming and machine learning M. Louboutin Ziyi Yin Rafael Orozco Thomas J. Grady Ali Siahkoohi G. Rizzuti Philipp A. Witte O. Møyner Gerard Gorman Felix J. Herrmann AI4CE 26 10 0 12 Apr 2023
Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation Joseph Cohen Xun Huan J. Ni 15 7 0 23 Mar 2023
Differentiable modeling to unify machine learning and physical models and advance Geosciences Chaopeng Shen A. Appling Pierre Gentine Toshiyuki Bandai H. Gupta ... Chris Rackauckas Tirthankar Roy Chonggang Xu Binayak Mohanty K. Lawson AI4CE 42 14 0 10 Jan 2023
Exploring Physical Latent Spaces for High-Resolution Flow Restoration Chloé Paliard Nils Thuerey Kiwon Um AI4CE 25 0 0 21 Nov 2022
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing Salah A. Faroughi N. Pawar C. Fernandes Maziar Raissi Subasish Das N. Kalantari S. K. Mahjour PINN AI4CE 27 49 0 14 Nov 2022
NeuralFMU: Presenting a workflow for integrating hybrid NeuralODEs into real world applications Tobias Thummerer Johannes Stoljar Lars Mikelsons AI4CE 40 9 0 08 Sep 2022
Differentiable Programming for Earth System Modeling Maximilian Gelbrecht Alistair J R White S. Bathiany Niklas Boers 21 16 0 29 Aug 2022
j-Wave: An open-source differentiable wave simulator A. Stanziola Simon Arridge B. Cox B. Treeby VLM 41 21 0 30 Jun 2022
E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations Jongwan Kim Dongjin Lee Byunggook Na Seongsik Park Jeonghee Jo Sung-Hoon Yoon 31 0 0 15 Jun 2022
Flexible Differentiable Optimization via Model Transformations Mathieu Besançon J. Garcia B. Legat Akshay Sharma 20 9 0 10 Jun 2022
Leveraging Stochastic Predictions of Bayesian Neural Networks for Fluid Simulations Maximilian Mueller Robin Greif Frank Jenko Nils Thuerey 24 3 0 02 May 2022
Machines of finite depth: towards a formalization of neural networks Pietro Vertechi M. Bergomi PINN 21 2 0 27 Apr 2022
A piece-wise constant approximation for non-conjugate Gaussian Process models Sarem Seitz 14 0 0 22 Apr 2022
Neural Ordinary Differential Equations for Nonlinear System Identification Aowabin Rahman Ján Drgoňa Aaron Tuor J. Strube 25 22 0 28 Feb 2022
AD-NEGF: An End-to-End Differentiable Quantum Transport Simulator for Sensitivity Analysis and Inverse Problems Ying Zhou Xiang Chen Peng Zhang Jun Wang Lei Wang Hongfeng Guo 29 1 0 10 Feb 2022
A research framework for writing differentiable PDE discretizations in JAX A. Stanziola Simon Arridge B. Cox B. Treeby 29 8 0 09 Nov 2021
NeuralFMU: Towards Structural Integration of FMUs into Neural Networks Tobias Thummerer Josef Kircher Lars Mikelsons AI4CE 23 8 0 09 Sep 2021
AdjointNet: Constraining machine learning models with physics-based codes S. Karra B. Ahmmed M. Mudunuru AI4CE PINN OOD 18 4 0 08 Sep 2021
Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting Stephan Thaler Julija Zavadlav 22 66 0 02 Jun 2021
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Winnie Xu Ricky T. Q. Chen Xuechen Li David Duvenaud BDL UQCV 27 46 0 12 Feb 2021
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients William S. Moses Valentin Churavy 13 83 0 04 Oct 2020
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics Li Li Stephan Hoyer Ryan Pederson Ruoxi Sun E. D. Cubuk Patrick F. Riley K. Burke AI4CE 35 120 0 17 Sep 2020
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs Beren Millidge Alexander Tschantz Christopher L. Buckley 30 118 0 07 Jun 2020
Automatic Differentiation in ROOT V. Vassilev A. Efremov O. Shadura PINN 16 5 0 09 Apr 2020
Scalable Gradients for Stochastic Differential Equations Xuechen Li Ting-Kam Leonard Wong Ricky T. Q. Chen David Duvenaud 17 310 0 05 Jan 2020
Graph Neural Ordinary Differential Equations Michael Poli Stefano Massaroli Junyoung Park Atsushi Yamashita Hajime Asama Jinkyoo Park AI4CE 51 154 0 18 Nov 2019
Neural Jump Stochastic Differential Equations J. Jia Austin R. Benson BDL 20 222 0 24 May 2019