466 citations · 945 across the 39 of their papers we have counts for
8 papers · 1 filter
Extending JumpProcess.jl for fast point process simulation with time-varying intensities
Guilherme Augusto Zagatti, Samuel A. Isaacson, Christopher Rackauckas +3
Point processes model the occurrence of a countable number of random points over some support. They can model diverse phenomena, such as chemical reactions, stock market transactio…
Differentiating Metropolis-Hastings to Optimize Intractable Densities
Gaurav Arya, Ruben Seyer, Frank Schäfer +7
We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete…
Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach
Vinicius V. Santana, Erbet Costa, Carine M. Rebello +3
This study presents a systematic machine learning approach for creating efficient hybrid models and discovering sorption uptake models in non-linear advection-diffusion-sorption sy…
Automated Translation and Accelerated Solving of Differential Equations on Multiple GPU Platforms
Utkarsh Utkarsh, Valentin Churavy, Yingbo Ma +8
We demonstrate a high-performance vendor-agnostic method for massively parallel solving of ensembles of ordinary differential equations (ODEs) and stochastic differential equations…
Performance Bounds for Quantum Feedback Control
Flemming Holtorf, Frank Schäfer, Julian Arnold +2
The limits of quantum feedback control have immediate consequences for quantum information science at large, yet remain largely unexplored. Here, we combine quantum filtering theor…
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas
Mohamed Tarek, Jose Storopoli, Casey Davis +4
This paper provides a comprehensive tutorial for Bayesian practitioners in pharmacometrics using Pumas workflows. We start by giving a brief motivation of Bayesian inference for ph…
Locally Regularized Neural Differential Equations: Some Black Boxes Were Meant to Remain Closed!
Avik Pal, Alan Edelman, Chris Rackauckas
Implicit layer deep learning techniques, like Neural Differential Equations, have become an important modeling framework due to their ability to adapt to new problems automatically…
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine +27
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powe…