130 citations · 426 across the 9 of their papers we have counts for
12 papers
Composing Modeling and Simulation with Machine Learning in Julia
Chris Rackauckas, Ranjan Anantharaman, Alan Edelman +10
In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build acce…
High-performance symbolic-numerics via multiple dispatch
Shashi Gowda, Yingbo Ma, Alessandro Cheli +4
As mathematical computing becomes more democratized in high-level languages, high-performance symbolic-numeric systems are necessary for domain scientists and engineers to get the…
Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
Avik Pal, Yingbo Ma, Viral Shah +1
Democratization of machine learning requires architectures that automatically adapt to new problems. Neural Differential Equations (NDEs) have emerged as a popular modeling framewo…
ModelingToolkit: A Composable Graph Transformation System For Equation-Based Modeling
Yingbo Ma, Shashi Gowda, Ranjan Anantharaman +3
Getting good performance out of numerical equation solvers requires that the user has provided stable and efficient functions representing their model. However, users should not be…
AutoMat: Accelerated Computational Electrochemical systems Discovery
Emil Annevelink, Rachel Kurchin, Eric Muckley +17
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and…
Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks
Ranjan Anantharaman, Yingbo Ma, Shashi Gowda +4
Modern design, control, and optimization often requires simulation of highly nonlinear models, leading to prohibitive computational costs. These costs can be amortized by evaluatin…
A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
Mike Innes, Alan Edelman, Keno Fischer +4
Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large amounts of data. At the same time, machine learning model…
Circuitscape in Julia: High Performance Connectivity Modelling to Support Conservation Decisions
Ranjan Anantharaman, Kimberly Hall, Viral Shah +1
Connectivity across landscapes influences a wide range of conservation-relevant ecological processes, including species movements, gene flow, and the spread of wildfire, pests, and…
Fashionable Modelling with Flux
Michael Innes, Elliot Saba, Keno Fischer +6
Machine learning as a discipline has seen an incredible surge of interest in recent years due in large part to a perfect storm of new theory, superior tooling, renewed interest in…
Julia: A Fresh Approach to Numerical Computing
Jeff Bezanson, Alan Edelman, Stefan Karpinski +1
Bridging cultures that have often been distant, Julia combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical…
Array operators using multiple dispatch: a design methodology for array implementations in dynamic languages
Jeff Bezanson, Jiahao Chen, Stefan Karpinski +2
Arrays are such a rich and fundamental data type that they tend to be built into a language, either in the compiler or in a large low-level library. Defining this functionality at…
Julia: A Fast Dynamic Language for Technical Computing
Jeff Bezanson, Stefan Karpinski, Viral B. Shah +1
Dynamic languages have become popular for scientific computing. They are generally considered highly productive, but lacking in performance. This paper presents Julia, a new dynami…