Universal differential equations for scientific machine learning C Rackauckas, Y Ma, J Martensen, C Warner, K Zubov, R Supekar, ... arXiv preprint arXiv:2001.04385, 2020 | 673 | 2020 |
A comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions Y Ma, V Dixit, MJ Innes, X Guo, C Rackauckas 2021 IEEE High Performance Extreme Computing Conference (HPEC), 1-9, 2021 | 152 | 2021 |
Diffeqflux. jl-A julia library for neural differential equations C Rackauckas, M Innes, Y Ma, J Bettencourt, L White, V Dixit arXiv preprint arXiv:1902.02376, 2019 | 152 | 2019 |
Stiff neural ordinary differential equations S Kim, W Ji, S Deng, Y Ma, C Rackauckas Chaos: An Interdisciplinary Journal of Nonlinear Science 31 (9), 2021 | 128 | 2021 |
Modelingtoolkit: A composable graph transformation system for equation-based modeling Y Ma, S Gowda, R Anantharaman, C Laughman, V Shah, C Rackauckas arXiv preprint arXiv:2103.05244, 2021 | 86 | 2021 |
Neuralpde: Automating physics-informed neural networks (pinns) with error approximations K Zubov, Z McCarthy, Y Ma, F Calisto, V Pagliarino, S Azeglio, L Bottero, ... arXiv preprint arXiv:2107.09443, 2021 | 81 | 2021 |
High-performance symbolic-numerics via multiple dispatch S Gowda, Y Ma, A Cheli, M Gwóźzdź, VB Shah, A Edelman, ... ACM Communications in Computer Algebra 55 (3), 92-96, 2022 | 61 | 2022 |
Opening the blackbox: Accelerating neural differential equations by regularizing internal solver heuristics A Pal, Y Ma, V Shah, CV Rackauckas International Conference on Machine Learning, 8325-8335, 2021 | 40 | 2021 |
Accelerated predictive healthcare analytics with pumas, a high performance pharmaceutical modeling and simulation platform C Rackauckas, Y Ma, A Noack, V Dixit, PK Mogensen, S Byrne, ... BioRxiv, 2020.11. 28.402297, 2020 | 40 | 2020 |
Accelerating simulation of stiff nonlinear systems using continuous-time echo state networks R Anantharaman, Y Ma, S Gowda, C Laughman, V Shah, A Edelman, ... arXiv preprint arXiv:2010.04004, 2020 | 30 | 2020 |
Universal differential equations for scientific machine learning. arXiv C Rackauckas, Y Ma, J Martensen, C Warner, K Zubov, R Supekar, ... arXiv preprint arXiv:2001.04385, 2020 | 28 | 2020 |
Composing modeling and simulation with machine learning in Julia C Rackauckas, M Gwozdz, A Jain, Y Ma, F Martinuzzi, U Rajput, E Saba, ... 2022 Annual Modeling and Simulation Conference (ANNSIM), 1-17, 2022 | 20 | 2022 |
Universal differential equations for scientific machine learning. arXiv 2020 C Rackauckas, Y Ma, J Martensen, C Warner, K Zubov, R Supekar, ... arXiv preprint arXiv:2001.04385, 2001 | 20 | 2001 |
Catalyst: fast biochemical modeling with Julia TE Loman, Y Ma, V Ilin, S Gowda, N Korsbo, N Yewale, C Rackauckas, ... bioRxiv, 2022.07. 30.502135, 2022 | 12 | 2022 |
jl-A julia library for neural differential equations. arXiv 2019 C Rackauckas, M Innes, Y Ma, J Bettencourt, L White, VD Dixit arXiv preprint arXiv:1902.02376, 0 | 10 | |
Sparsity programming: Automated sparsity-aware optimizations in differentiable programming S Gowda, Y Ma, V Churavy, A Edelman, C Rackauckas Program Transformations for ML Workshop at NeurIPS 2019, 2019 | 7 | 2019 |
Universal differential equations for scientific machine learning. arXiv preprint 2020 C Rackauckas, Y Ma, J Martensen, C Warner, K Zubov, R Supekar, ... arXiv preprint arXiv:2001.04385, 2001 | 7 | 2001 |
Automated translation and accelerated solving of differential equations on multiple GPU platforms U Utkarsh, V Churavy, Y Ma, T Besard, P Srisuma, T Gymnich, ... Computer Methods in Applied Mechanics and Engineering 419, 116591, 2024 | 6 | 2024 |
Constrained smoothers for state estimation of vapor compression cycles VM Deshpande, CR Laughman, Y Ma, C Rackauckas 2022 American Control Conference (ACC), 2333-2340, 2022 | 6 | 2022 |
Catalyst: Fast and flexible modeling of reaction networks TE Loman, Y Ma, V Ilin, S Gowda, N Korsbo, N Yewale, C Rackauckas, ... PLOS Computational Biology 19 (10), e1011530, 2023 | 5 | 2023 |