Last week, members of the TAMIDS team and the Student Ambassador program hosted a week-long scientific machine learning summer school! Several expert guest speakers from universities across the state attended to share their research and the fundamentals of physics-informed neural networks and machine learning. The presentations spanned topics such as astronomy, neural networks, and supervised learning, and showcased the versatile applications of machine learning.
Participants attended lectures that focused on the foundational concepts of physics-informed neural networks and scientific machine learning with hands-on tutorials that reinforced these concepts. At the end of the week, students received a certificate of completion and participated in a poster session where they displayed their research and scientific machine learning methods.
Presentations and Topics
- Lifan Wang: Physics and AI: Forward and Backward Modeling of Time Sequence Supernova Observations
- Jonathan Siegel: Approximation Theory for Symmetry-Preserving Neural Networks
- Andreas Mang: Data- and model-driven approaches for solving inverse problems
- Andrea Bonito: Convergence and Error Control of Consistent PINNs for Elliptic PDEs
- Ulisses Braga-Neto: DeepOSets for In-Context Supervised Learning and PDE Operator Learning.
- Thomas O’Leary-Roseberry: Derivative-Informed Neural Operators
- Raymundo Arroyave: Toward AI-enabled Autonomy in Alloy Discovery
- Nicolas Charon: Machine Learning for Geometric Data
- Eduardo Gildin: Applications of SciML in Surrogate Reservoir Simulation Models
This workshop was made possible by the generous contributions from the Los Alamos National Lab and the NASA-DEAP Institute in Research and Education for Science Translation via Low-Resource Neural Machine Translation award (Project Number: 80NSSC22KM0052) and the collaboration of Prairie View A&M University (PVAMU), Texas Southern University (TSU), and Texas A&M University (TAMU).
Event Organizers
- Debasish Mishra, Senior Data Science Ambassador: Biological and Agricultural Engineering, College of Agriculture and Life Sciences – debmishra@tamu.edu
- Ming Zhong, Assistant Professor: Mathematics, University of Houston – mzhong4@uh.edu
- Xingzhuo Chen, Postdoctoral Research Associate: TAMIDS SciML Lab – chenxingzhuo@tamu.edu
- Suparno Bhattacharyya, Assistant Research Scientist: TAMIDS Digital Twin Lab – suparnob@tamu.edu
- Drew Casey, Assistant Director for Program Engagement: TAMIDS Assistant Director for Program Engagement – drew.casey@tamu.edu