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 …

Accurate prediction of dynamical systems in unstructured meshes has recently shown
successes in scientific simulations. Many dynamical systems have a nonnegligible level of …

Thermal analysis provides deeper insights into electronic chips' behavior under different
temperature scenarios and enables faster design exploration. However, obtaining detailed …

In a preliminary attempt to address the problem of data scarcity in physics-based machine
learning, we introduce a novel methodology for data generation in physics-based …

This paper presents a methodology to learn surrogate models of steady state fluid dynamics
simulations on meshed domains, based on Implicit Neural Representations (INRs). The …

Static chip thermal analysis provides detailed and accurate thermal profile on chip. The chip
power map, commonly modeled as rectangular regions of distinct heat sources, significantly …

We propose a geometry-to-flow diffusion model that utilizes the input of obstacle shape to
predict a flow field past the obstacle. The model is based on a learnable Markov transition …

Multiphysics simulation, which models the interactions between multiple physical processes,
and multi-component simulation of complex structures are critical in fields like nuclear and …

We present an unsupervised deep learning method to perform flow denoising and super-
resolution without high-resolution labels. We demonstrate the ability of a single model to …

Full-chip transient thermal simulation, which is essential for solving pressing thermal issues,
is time-consuming and resource-intensive, especially when nonlinear effects including …