Workshop Description
The Spring 2026 TAMIDS SciML workshop will be an in-person afternoon event in Blocker 220 on Wednesday, April 29, from 2:00 pm – 5:00 pm. The aim of this informal workshop is to showcase work by the Texas A&M community on scientific machine learning and to foster the formation of new collaborations. Areas of interest include:
- Physics-informed deep neural networks
- Physics-informed Gaussian processes
- Bayesian filtering and inference in physical models
- Data-driven model discovery through large-scale simulation
- ML-guided acceleration of numerical simulations
- Agentic AI for SciML
- Scientific and Engineering applications
Workshop Organization
The workshop is open to all members of Texas A&M. The workshop will comprise short invited talks from Texas A&M speakers, which will consist of a 10-minute presentation plus 5-minute technical discussion. A 30-minute coffee break is included to foster networking and collaboration. For questions, please contact the workshop organizers: Ulisses Braga Neto (ulisses@tamu.edu) and Drew Casey (drew.casey@tamu.edu)
Workshop Schedule
| 1:50-2:00 | Check-In |
| 2:00-2:05 | Welcome and Introduction: Ulisses Braga-Neto, TAMIDS SciML Lab Director, and Drew Casey, TAMIDS Associate Director |
| 2:05-2:20 | Jian Tao, Assistant Professor, Visualization Sensor Optimization with Reinforcement Learning: From Static Design to Adaptive Intelligence |
| 2:20-2:35 | Danilo Silva, Associate Professor, Federal University of Santa Catarina (Brazil) Evaluating the Quality of an Uncertainty Estimator using Selective Prediction |
| 2:35-2:50 | Shakti Padhy, Post-Doctoral Researcher, Materials Science & Engineering Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery |
| 2:50-3:05 | Aditya Nambiar, Ph.D. Student, Mathematics Universality for In-Context Multi Operator Learning |
| 3:05-3:35 | Coffee Break |
| 3:35-3:50 | Gesa Chen, TAMIDS Post-Doctoral Researcher Convolution Operator Network for Forward and Inverse Problems (FI-Conv): Application to Plasma Turbulence Simulation |
| 3:50-4:05 | Átila Luna, Ph.D. Student, PUC-Rio de Janeiro (Brazil) Solver-in-the-Loop: A Consistent Training Method for Neural Operators |
| 4:05-4:20 | Luís Loo, Ph.D. Student, Electrical and Computer Engineering Neural Operator Discovery via AI Scientific Community: Virtual Lab Swarms for Operator Learning |
| 4:20-4:35 | Durward “Tripp” Cator III, Ph.D. Student, Electrical and Computer Engineering Probabilistic Model Order Reduction for Bayesian PDE Solvers |
| 4:35-4:40 | Closing Remarks: Ulisses Braga-Neto, TAMIDS SciML Lab Director |