Christopher Vincent Rackauckas

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Yingbo MaJulia ComputingDirección de correo verificada de juliacomputing.com
Alan EdelmanProfessor of Applied Mathematics, Member Computer Science AI LABS, MITDirección de correo verificada de mit.edu
Qing NieUniversity of California, IrvineDirección de correo verificada de uci.edu
Viral B. ShahCo-founder and CEO, JuliaHubDirección de correo verificada de juliahub.com
Raj DandekarMassachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Vaibhav DixitMITDirección de correo verificada de mit.edu
Mike InnesIndependentDirección de correo verificada de csail.mit.edu
Kirill ZubovResearch Engineer MIT Julia LabDirección de correo verificada de csail.mit.edu
Carl Julius MartensenOtto-von-Guericke UniversityDirección de correo verificada de ovgu.de
Ali RamadhanGraduate researcher, Massachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Rohit SupekarThe New York TimesDirección de correo verificada de alum.mit.edu
Ranjan AnantharamanJuliaHub Inc.Dirección de correo verificada de mit.edu
George BarbastathisProfessor of Mechanical Engineering, Massachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Elliot SabaUniversity of WashingtonDirección de correo verificada de uw.edu
Dominic J. SkinnerFlatiron InstituteDirección de correo verificada de mit.edu
Avik PalPh.D. Student @ Massachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Keno FischerJuliaHub, Inc.Dirección de correo verificada de juliahub.com
Frank SchäferMassachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Christopher LaughmanSenior Principal Research Staff, Mitsubishi Electric Research LaboratoriesDirección de correo verificada de merl.com
Vijay IvaturiCenter for Pharmacometrics, Manipal UniversityDirección de correo verificada de manipal.edu

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Christopher Vincent Rackauckas

Massachusetts Institute of Technology

Dirección de correo verificada de mit.edu - Página principal

Scientific Machine Learning SciML Numerical Differential Equations Stochastic Biology


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
DifferentialEquations.jl – a performant and feature-rich ecosystem for solving differential equations in Julia C Rackauckas, Q Nie Journal of Open Research Software 5 (1), 2017	1730	2017
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	968*	2020
Generalized physics-informed learning through language-wide differentiable programming C Rackauckas, A Edelman, K Fischer, M Innes, E Saba, VB Shah, ... 2020 AAAI Spring Symposium on Machine Learning for Physical Sciences (MLPS), 2021	273*	2021
A machine learning-aided global diagnostic and comparative tool to assess effect of quarantine control in COVID-19 spread R Dandekar, C Rackauckas, G Barbastathis Patterns 1 (9), 2020	248*	2020
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	191	2021
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	181	2021
Differentiable modelling to unify machine learning and physical models for geosciences C Shen, AP Appling, P Gentine, T Bandai, H Gupta, A Tartakovsky, ... Nature Reviews Earth & Environment 4 (8), 552-567, 2023	172	2023
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	123	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	112	2021
Adaptive methods for stochastic differential equations via natural embeddings and rejection sampling with memory C Rackauckas, Q Nie Discrete and continuous dynamical systems. Series B 22 (7), 2731, 2017	94	2017
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	93	2022
Bayesian neural ordinary differential equations R Dandekar, K Chung, V Dixit, M Tarek, A Garcia-Valadez, KV Vemula, ... Languages for Inference (LAFI) 2021 - POPL 2021, 2020	77	2020
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, ...	67	2020
Plots.jl--a user extendable plotting API for the julia programming language S Christ, D Schwabeneder, C Rackauckas arXiv preprint arXiv:2204.08775, 2022	56	2022
Julia for biologists E Roesch, JG Greener, AL MacLean, H Nassar, C Rackauckas, TE Holy, ... Nature methods 20 (5), 655-664, 2023	54	2023
Automatic differentiation of programs with discrete randomness G Arya, M Schauer, F Schäfer, C Rackauckas Advances in Neural Information Processing Systems 35, 10435-10447, 2022	49	2022
Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics A Pal, Y Ma, V Shah, C Rackauckas The Thirty-eighth International Conference on Machine Learning (ICML), 2021	46	2021
Confederated modular differential equation APIs for accelerated algorithm development and benchmarking C Rackauckas, Q Nie Advances in Engineering Software 132, 1-6, 2019	46	2019
Noise modulation in retinoic acid signaling sharpens segmental boundaries of gene expression in the embryonic zebrafish hindbrain J Sosnik, L Zheng, CV Rackauckas, M Digman, E Gratton, Q Nie, ... Elife 5, e14034, 2016	46	2016
Stability-optimized high order methods and stiffness detection for pathwise stiff stochastic differential equations C Rackauckas, Q Nie 2020 IEEE High Performance Extreme Computing Conference (HPEC), 1-8, 2020	41	2020

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