General Science

This work presents a high-fidelity simulation framework for modeling large-scale wildfire scenarios that take into consideration realistic topographies, atmospheric conditions, turbulence/fire interaction, and flow dynamics. With the overall goal of enabling large-scale ensemble simulations and the integration of the simulation results into machine-learning applications, this modeling framework...

A computational fluid dynamics (CFD) simulation framework for predicting complex flows is developed on the Tensor Processing Unit (TPU) platform. The TPU architecture is featured with accelerated performance of dense matrix multiplication, large high bandwidth memory, and a fast inter-chip interconnect, which makes it appealing for scientific high performance computing (HPC). This CFD framework...

DNA-Encoded Libraries (DEL) data, often with millions of data points, enables large deep learning models to make real contributions in drug discovery (e.g., hit-finding). The state-of-the-art method of modeling DEL data, GCNN multiclass model, requires domain experts to create mutually exclusive classification labels from multiple selection readouts of DEL data, which is not always an optimal...

The concern of overconfident mis-predictions under distributional shift demands extensive reliability research on Graph Neural Networks used in critical tasks in drug discovery. Here we first introduce CardioTox, a real-world benchmark on drug cardio-toxicity to facilitate such efforts. Our exploratory study shows overconfident mis-predictions are often distant from training data. That leads us...