One of the most promising applications of machine learning in computational physics is to
accelerate the solution of partial differential equations (PDEs). The key objective of machine …

R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in
experimental populations. The R/qtl2 software expands the scope of the widely-used R/qtl …

Stan is a probabilistic programming language for specifying statistical models. A Stan
program imperatively defines a log probability function over parameters conditioned on …

Sparse tensors arise in problems in science, engineering, machine learning, and data
analytics. Programs that operate on such tensors can exploit sparsity to reduce storage …

I consider the development of Markov chain Monte Carlo (MCMC) methods, from late-1980s
Gibbs sampling to present-day gradient-based methods and piecewise-deterministic Markov …

Deep learning has had remarkable success in robotic perception, but its data-centric nature
suffers when it comes to generalizing to ever-changing environments. By contrast, physics …

In this paper, we address the question of how to automatically map computational kernels to
highly efficient code for a wide range of computing platforms and establish the correctness of …

Simulating stiff materials in applications where deformations are either not significant or can
safely be ignored is a pivotal task across fields. Rigid body modeling has thus long …

As computational challenges in optimization and statistical inference grow ever harder,
algorithms that utilize derivatives are becoming increasingly more important. The …

This paper provides a new avenue for exploiting deep neural networks to improve physics-
based simulation. Specifically, we integrate the classic Lagrangian mechanics with a deep …