Christopher Rackauckas
Orcid: 0000-0001-5850-0663Affiliations:
- Massachusetts Institute of Technology, Department of Mathematics, Cambridge, MA, USA
According to our database1,
Christopher Rackauckas
authored at least 48 papers
between 2018 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
-
on dl.acm.org
On csauthors.net:
Bibliography
2024
NonlinearSolve.jl: High-Performance and Robust Solvers for Systems of Nonlinear Equations in Julia.
CoRR, 2024
2023
Nat. Mac. Intell., December, 2023
PLoS Comput. Biol., October, 2023
Extending JumpProcess.jl for fast point process simulation with time-varying intensities.
CoRR, 2023
Automated Translation and Accelerated Solving of Differential Equations on Multiple GPU Platforms.
CoRR, 2023
CoRR, 2023
Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach.
CoRR, 2023
Differentiable modeling to unify machine learning and physical models and advance Geosciences.
CoRR, 2023
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023
Locally Regularized Neural Differential Equations: Some Black Boxes were meant to remain closed!
Proceedings of the International Conference on Machine Learning, 2023
Continuous Deep Equilibrium Models: Training Neural ODEs Faster by Integrating Them to Infinity.
Proceedings of the IEEE High Performance Extreme Computing Conference, 2023
2022
PLoS Comput. Biol., 2022
GlobalSensitivity.jl: Performant and Parallel Global Sensitivity Analysis with Julia.
J. Open Source Softw., 2022
ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models.
J. Mach. Learn. Res., 2022
DelayDiffEq: Generating Delay Differential Equation Solvers via Recursive Embedding of Ordinary Differential Equation Solvers.
CoRR, 2022
Parallelizing Explicit and Implicit Extrapolation Methods for Ordinary Differential Equations.
CoRR, 2022
CoRR, 2022
Mixing Implicit and Explicit Deep Learning with Skip DEQs and Infinite Time Neural ODEs (Continuous DEQs).
CoRR, 2022
ACM Commun. Comput. Algebra, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Parallelizing Explicit and Implicit Extrapolation Methods for Ordinary Differential Equations.
Proceedings of the IEEE High Performance Extreme Computing Conference, 2022
Proceedings of the Annual Modeling and Simulation Conference, 2022
Proceedings of the American Control Conference, 2022
2021
Safe Blues: The case for virtual safe virus spread in the long-term fight against epidemics.
Patterns, 2021
CoRR, 2021
NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations.
CoRR, 2021
ModelingToolkit: A Composable Graph Transformation System For Equation-Based Modeling.
CoRR, 2021
ACM Commun. Comput. Algebra, 2021
Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics.
Proceedings of the 38th International Conference on Machine Learning, 2021
A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions.
Proceedings of the 2021 IEEE High Performance Extreme Computing Conference, 2021
Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks.
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021
2020
A Machine Learning-Aided Global Diagnostic and Comparative Tool to Assess Effect of Quarantine Control in COVID-19 Spread.
Patterns, 2020
Stability-Optimized High Order Methods and Stiffness Detection for Pathwise Stiff Stochastic Differential Equations.
Proceedings of the 2020 IEEE High Performance Extreme Computing Conference, 2020
Generalized Physics-Informed Learning through Language-Wide Differentiable Programming.
Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 23rd - to, 2020
2019
A Differentiable Programming System to Bridge Machine Learning and Scientific Computing.
CoRR, 2019
Confederated modular differential equation APIs for accelerated algorithm development and benchmarking.
Adv. Eng. Softw., 2019
2018
A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions.
CoRR, 2018