Data Analytics and Machine Learning

MiC Seminars

Purpose of the Seminars The Mathematics in Computation (MiC) seminar series occurs every Thursday, 3 - 4 pm EST and hosts speakers from various areas of research aligning with the staff from the…
A stochastic optimal control framework for quantifying and reducing uncertainties in deep learning

We propose to develop a stochastic optimal control framework for quantifying and reducing uncertainties in deep learning by exploiting the connection between probabilistic network architectures and…
Black-box training for scientific machine learning models

Project Summary: We propose to develop a scalable black-box training framework for scientific machine learning (SciML) models that are non-trainable with existing automatic differentiation-based…
FASTMath

The FASTMath SciDAC Institute develops and deploys scalable mathematical algorithms and software tools for reliable simulation of complex physical phenomena and collaborates with application…
Environment for Quantifying Uncertainty: Integrated aNd Optimized at the eXtreme scale (Equinox)

The goal of this project is to establish a modern mathematical and statistical foundation that will enable next-generation, complex, stochastic predictive simulations. Such a foundation is critical…