Rackauckas, Chris
- A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
2019/07/17 by Mike Innes, Alan Edelman, Innes, Mike +12 · 2 voices · 13 citations
Computer Science · #Neural Networks and Applications
- NonlinearSolve.jl: High-Performance and Robust Solvers for Systems of Nonlinear Equations in Julia
2024/03/25 by Avik Pal, Pal, Avik, Flemming Holtorf +20 · 3 voices · 5 citations
Computer Science · Physics and Astronomy · #Parallel Computing and Optimization Techniques #Polynomial and algebraic computation #Quantum chaos and dynamical systems #math.NA
- C codegen considered unnecessary: go directly to binary, do not pass C. Compilation of Julia code for deployment in model-based engineering
2025/02/03 by Fredrik Bagge Carlson, Cody Tapscott, Carlson, Fredrik Bagge +6 · 5 voices · 2 citations
Computer Science · Decision Sciences · #Model-Driven Software Engineering Techniques #Simulation Techniques and Applications #Modeling and Simulation Systems
- Automatic Differentiation of Programs with Discrete Randomness
2022/10/16 by Gaurav Arya, Moritz Schauer, Arya, Gaurav +6 · 2 voices · 5 citations
Computer Science · Decision Sciences · #cs.LG #cs.MS #math.NA #math.PR
- ModelingToolkit: A Composable Graph Transformation System For Equation-Based Modeling
2021/03/09 by Ma, Yingbo, Gowda, Shashi, Anantharaman, Ranjan +3 · 6 citations
#FOS: Computer and information sciences #Mathematical Software (cs.MS) #Software Engineering (cs.SE) #Symbolic Computation (cs.SC)
- NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations
2021/07/19 by Zubov, Kirill, McCarthy, Zoe, Ma, Yingbo +11 · 6 citations
#FOS: Computer and information sciences #Mathematical Software (cs.MS) #Symbolic Computation (cs.SC)
- Bayesian Neural Ordinary Differential Equations
2020/12/14 by Raj Dandekar, Dandekar, Raj, Chung, Karen +8 · 5 citations
Physics and Astronomy · Computer Science · Mathematics · #Model Reduction and Neural Networks #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods
- DiffEqFlux.jl - A Julia Library for Neural Differential Equations
2019/02/06 by Rackauckas, Chris, Innes, Mike, Ma, Yingbo +3 · 2 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Symbolic-Numeric Integration of Univariate Expressions based on Sparse Regression
2022/01/29 by Shahriar Iravanian, Carl Julius Martensen, Iravanian, Shahriar +11 · 1 voice
#cs.SC
- Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks
2020/10/07 by Ranjan Anantharaman, Yingbo Ma, Anantharaman, Ranjan +11 · 1 citation
Computer Science · Physics and Astronomy · #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Neural Networks and Applications #Neural Networks and Reservoir Computing
- Composing Modeling and Simulation with Machine Learning in Julia
2021/05/12 by Christopher Rackauckas, Rackauckas, Chris, Ranjan Anantharaman +23 · 1 citation
Computer Science · Physics and Astronomy · Engineering · #Neural Networks and Reservoir Computing #Model Reduction and Neural Networks #Optical Network Technologies
- Locally Regularized Neural Differential Equations: Some Black Boxes Were Meant to Remain Closed!
2023/03/03 by Pal, Avik, Edelman, Alan, Rackauckas, Chris · 1 citation
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Numerical Analysis (math.NA)
- Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach
2023/03/22 by Santana, Vinicius V., Costa, Erbet, Rebello, Carine M. +3 · 1 citation
#Computational Engineering #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #and Science (cs.CE)