Scientific Machine Learning (SciML) Summer School 2025 

Location: Texas A&M University, Blocker Building, Room 102

This free five-day summer school is designed to introduce graduate students to the fundamentals of Physics-Informed Neural Networks (PINNs) and Scientific Machine Learning (SciML). The first three days will focus on foundational concepts in PINNs, structured into morning theory sessions followed by afternoon hands-on tutorial sessions to reinforce practical understanding. The final two days will feature invited speakers who will present their research on SciML, showcasing its applications across various domains. 

This program offers students lunch and coffee.  

Schedule

DAY	AGENDA
Monday (May 12)	– Introduction of Differential Equation Modeling – Introduction of Numerical ODE (Euler’s Method, Runge-Kutta, Multi-step, Explicit Euler, and probably some SDE) – Introduction of Numerical PDE (Finite Difference, Finite Element, and Spectral Method) on Elliptical and parabolic PDEs, with some touch on hyperbolic.
Tuesday (May 13)	– Introduction of Scientific Machine Learning – Introduction of Physics Informed Neural Networks – Introduction of Physics Informed Gaussian Processes
Wednesday (May 14)	– Introduction to Inverse Problems – Inverse Problems with PINNs and PIGPs – Inverse Problems with SINDy and Neural ODEs
Thursday (May 15)	Invited Guest Speakers 9:10 – 10:10 AM — Lifan Wang, Physics and AI: Forward and Backward Modeling of Time Sequence Supernova Observations 10:20 – 11:20 AM — Jonathan Siegel, Approximation Theory for Symmetry Preserving Neural Networks 11:30 AM – 12:30 PM — Andreas Mang, Data- and model-driven approaches for solving inverse problems 1:40 – 2:40 PM — Andrea Bonito, Convergence and Error Control of Consistent PINNs for Elliptic PDEs 2:50 – 3:50 PM — Ulisses Braga-Neto, DeepOSets for In-Context Supervised Learning and PDE Operator Learning. 4:00 – 5:00 PM — Thomas O’Leary-Roseberry, Derivative-Informed Neural Operators
Friday (May 16)	Invited Guest Speakers 9:10 – 10:10 AM — Raymundo Arroyave, Toward AI-enabled Autonomy in Alloy Discovery 10:20 – 11:20 AM — Nicolas Charon, Machine learning for geometric data 11:30 AM – 12:30 PM — Eduardo Gildin, Applications of SciML in Surrogate Reservoir Simulation Models Student Showcase 1:00 – 3:00 PM — Poster Session and Networking Event

Guest Speakers

• Jonathan Siegel, Assistant Professor, Mathematics, Texas A&M University
• Lifan Wang, Professor, Physics & Astronomy, Texas A&M University
• Raymundo Arroyave, Professor, Materials Science & Engineering, Texas A&M University
• Andrea Bonito, Professor, Mathematics, Texas A&M University
• Eduardo Gildin, Professor, Petroleum Engineering, Texas A&M University
• Andreas Mang, Associate Professor, Mathematics, University of Houston
• Ulisses Braga-Neto, Professor, Electrical & Computer Engineering, Texas A&M University
• Thomas O’Leary-Roseberry, Research Associate, University of Texas Austin
• Nicolas Charon, Assistant Professor, University of Houston

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, Digital Twin Lab; suparnob@tamu.edu
• Drew Casey, Assistant Director for Program Engagement, TAMIDS; drew.casey@tamu.edu

Registration Details

To promote engagement between instructors and participating graduate students, the cohort size will be limited to 40 students selected from the pool of workshop applicants. The registration deadline is April 18, 2025, at 11:59 PM. Late submissions will not be accepted.

Please read the application instructions before applying.

This workshop is possible due to 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).