Scientific Machine Learning Lab
The Scientific Machine Learning (SciML) Lab aims at bringing together a multidisciplinary team of scientists, mathematicians, physicists and engineers that cut across several Colleges and Departments at Texas A&M, who share a unified goal of building physics-aware machine learning architectures from the underlying physical phenomena.
About
Scientific Machine Learning (SciML) is an emerging area that brings together the fields of Machine Learning and Scientific Computation. SciML introduces scientific model constraints in Machine Learning algorithms, allowing prediction of future performance of complex multiscale, multiphysics systems using sparse, low-fidelity, and heterogeneous data. Unlike traditional black-box Machine Learning methods, SciML aims to deliver interpretable models, leading to improved verification and validation in mission-critical applications
Resources
Research Projects
Our goals in this project concern new architectures and new algorithms for physics-informed neural networks, neural operators, and Bayesian models.
This project explores new approaches to physics-based and data-driven models and combinations thereof that effectively embeds physical constraints, conservation laws and constitutive relations with data for robust upscaling, sensible time-stepping, accurate proxy models and efficient optimization schemes.
This project will lead to new approaches to Type Ia supernovae (SN~Ia) cosmology that can quantitatively incorporate the latest in observational and theoretical developments using SciML tools.
This project is a recent collaboration with the Institute of Fusion Sciences at UT-Austin, Virginia Tech, and VTT (Finland). The goal is to develop deep learning surrogates for fast simulation of plasma turbulence in fusion devices.
The goal of this project is to develop novel scientific machine learning (SciML) algorithms and to apply them to highly coupled multi-physics thermodynamically consistent phase field models for microstructure evolution, in order to deliver a set of efficient and accurate physics-aware forward and inverse models of processing-microstructure relationships in nanostructured composite thermoelectric materials, together with reliable uncertainty quantification metrics to support decision making in the design of these materials.
Contact
Collaboration requests should be made to individual SciML Lab researchers.
Ulisses Braga-Neto
Organizational questions concerning SciML Lab
ContactLevi McClenny
Website update requests for research, events, or announcements
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