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03/31/2023
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas
This paper provides a comprehensive tutorial for Bayesian practitioners ...
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03/22/2023
Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach
This study presents a systematic machine learning approach for creating ...
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03/03/2023
Locally Regularized Neural Differential Equations: Some Black Boxes Were Meant to Remain Closed!
Implicit layer deep learning techniques, like Neural Differential Equati...
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01/10/2023
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceiv...
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10/16/2022
Automatic Differentiation of Programs with Discrete Randomness
Automatic differentiation (AD), a technique for constructing new program...
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08/26/2022
DelayDiffEq: Generating Delay Differential Equation Solvers via Recursive Embedding of Ordinary Differential Equation Solvers
Traditional solvers for delay differential equations (DDEs) are designed...
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04/08/2022
ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models
We introduce ReservoirComputing.jl, an open source Julia library for res...
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01/29/2022
Symbolic-Numeric Integration of Univariate Expressions based on Sparse Regression
Most computer algebra systems (CAS) support symbolic integration as core...
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11/10/2021
Physics-enhanced deep surrogates for PDEs
We present a "physics-enhanced deep-surrogate ("PEDS") approach towards ...
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09/25/2021
AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia
No single Automatic Differentiation (AD) system is the optimal choice fo...
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05/12/2021
Composing Modeling and Simulation with Machine Learning in Julia
In this paper we introduce JuliaSim, a high-performance programming envi...
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03/09/2021
ModelingToolkit: A Composable Graph Transformation System For Equation-Based Modeling
Getting good performance out of numerical equation solvers requires that...
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12/14/2020
Bayesian Neural Ordinary Differential Equations
Recently, Neural Ordinary Differential Equations has emerged as a powerf...
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10/07/2020
Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks
Modern design, control, and optimization often requires simulation of hi...
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07/17/2019
A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
Scientific computing is increasingly incorporating the advancements in m...
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02/06/2019