Abstract: Upcoming exascale architectures require rethinking of the numerical algorithms used in large-scale PDE-based applications. These architectures favor algorithms, such as high-order finite elements, that expose fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. In this talk we present an overview of MFEM , a scalable library for […]
Grace Hopper Lecture Position: Giulia Galli, Liew Family Professor, University of Chicago and Argonne National Laboratory Abstract: In this talk, I will describe theoretical and computational strategies based on quantum mechanical calculations, aimed at predicting material properties suitable for the development of quantum technologies. Specifically, I will present results obtained on both classical and near-term […]
Speaker: Patrick Kidger, Mathematician at Google X Abstract: This is a quick introduction to neural ODEs for scientific applications. The goal is to (a) provide a modelling tool that enhances the expressivity of existing theory-driven approaches, (b) demonstrate that neural ODEs are easy to use via modern autodifferentiable software, and (c) give enough of the […]
Speaker: William Noid, Professor of Chemistry at Penn State University Abstract: Many studies adopt low-resolution, coarse-grained (CG) models to investigate polymers, proteins, and other soft materials. These studies must first specify the details that are retained in the low-resolution model, i.e., they must specify the “CG representation.” Unfortunately, the “best” representation for complex systems is […]
Speaker: Jane Bae, Assistant Professor of Aerospace at Caltech Abstract: The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and weather prediction, hinge on the choice of turbulence models. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to turbulence modeling efforts. However, simulations […]
Title: Genetic testing and adverse selection Speaker: Eduardo Azevedo, Professor of Business Economics and Public Policy at Wharton, John M. Bendheim Professor, Thomas L. Bendheim Professor Abstract: Technology is dramatically driving down the cost of sequencing genetic data and increasing the quality of predictions made with this data. A standard concern is that these predictions […]
Title: Modeling Lone Pair Dynamics in Materials Speaker: Richard Remsing, Assistant Professor of Chemistry and Chemical Biology at Rutgers University Abstract: Materials properties are governed by the structure and dynamics of the bonds between their constituent atoms. In addition to covalent, metallic, and ionic interactions that we typically think about, lone pair electrons can result in […]
Title: Physics-compatible kinetic-energy and entropy preserving (KEEP) scheme for high-fidelity simulation of compressible turbulence Speaker: Soshi Kawai, Professor of Aerospace Engineering at Tohoku University Abstract: Low (or ideally zero) numerical dissipation is always critical for high-fidelity scale-resolving flow simulations, as numerical dissipation prevents the physics of inviscid kinetic energy and entropy conservation, which is an […]
Title: Exploiting time-domain parallelism to accelerate neural network training and PDE constrained optimization Speaker: Eric Cyr, Principal Member of the Technical Staff at the Computer Science Research Institute at Sandia National Laboratories Abstract: This talk will explore methods for accelerating numerical optimization constrained by transient problems using parallelism. Two types of transient problems will be […]
Speaker: Neel Dey, Postdoctoral Associate in the Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory Abstract: Current medical image analysis projects involve months to years of data annotation and custom technical development. This talk introduces methods to train networks that generalize out-of-the-box to new modalities, anatomies, and datasets all without retraining for the […]