TAMIDS SciML Lab Seminar Series: Youngsoo Choi: Reusable and Extrapolative Foundation Models for Scientific Simulations
Dr. Choi is is a staff scientist at Lawrence Livermore National Laboratory’s CASC group. He earned his BS from Cornell and his PhD from Stanford. His research focuses on surrogate and reduced-order models in inverse problems, design optimization, and uncertainty quantification. He is the lead developer of the open-source libROM project in data-driven surrogate modeling. The SciML Lab is looking forward to his visit.
TAMIDS Spring 2025 SciML Workshop
The SciML Lab is hosting a one-day workshop on Scientific Machine Learning on April 17, in Rudder 302. The workshop is open to all members of Texas A&M (registration is required). The workshop will comprise a keynote talk by TAMIDS SciML visitor Simo Särkkä (Aalto University, Finland) and short invited talks from Texas A&M speakers. Two 30-minute coffee breaks and a lunch break are included, in order to foster networking and collaboration.
TAMIDS SciML Lab Seminar Series: George Biros: Graph Neural Nets for Stochastic Phase Field PDEs
The SciML Lab is pleased to receive the visit of George Biros. He is the W. A. “Tex” Moncrief Chair in Simulation-Based Engineering Sciences at the Oden Institute, and Professor of Mechanical Engineering, University of Texas at Austin. Dr. Biros is going to talk about his cutting-edge research on training neural operators to solve stochastic phase-field PDEs occurring in materials science problems.
TAMIDS SciML Lab Seminar Series: Aaro Järvinen: Accelerating Fusion Energy Development with Scientific Machine Learning
Dr. Järvinen is a Senior Scientist at VTT Finland, where he manages the DEEPlasma Project, an international collaboration involving researchers from Finland and Texas, including the SciML Lab, with the goal of developing new, groundbreaking AI/ML methods for plasma physics relevant to fusion reactor operation.
TAMIDS SciML Lab Seminar Series: David Hatch: Towards Commercially Viable Fusion Energy: Innovation, Challenges, and the Growing Contributions from AI/ML
The SciML Lab is glad to host Dr. David Hatch. He is a research professor at the Institute for Fusion Studies at the University of Texas at Austin. He is a well-known expert on fundamental and applied aspects of fusion plasma physics. The SciML Lab is collaborating with Dr. Hatch and other partners on the DEEPlasma project to develop next-generation machine learning methods to enhance the operation of fusion reactors.
Spring 2024 Scientific Machine Learning Class
The Spring 2024 iteration of the ECEN 689 Scientific Machine Learning class is open for enrollment to Texas A&M undergraduate and graduate students.
New Class on Physics-based and Data-Driven Reduced-Order Modeling for Engineering Systems
Scientific Machine Learning lab members Eduardo Gildin and Jean Ragusa are offering a new graduate class in the Fall 2023 semester.
TAMIDS SciML Lab Seminar Series: Yannis Kevrekidis: No Equations, No Variables, No Space, No Time:Data and the Modeling of Complex Systems
The SciML Lab is glad to host Dr. Kevrekidis on March 27. He is a Bloomberg Distinguished Professor at Johns Hopkins University, after a 31-year career at Princeton. Dr. Kevrekidis is a world-renowned expert in nonlinear dynamics, and more recently he has devoted his interests to scientific machine learning. He is a former Packard Fellow, Presidential Young Investigator, and Ulam Scholar at Los Alamos National Laboratory, and is a member of the NAE, the AAS, and the Academy of Athens.
TAMIDS 2022 SciML Workshop
The 2022 edition of the annual SciML Lab research workshop will be held in the morning of Tuesday Nov 8 in Blocker 220. The aim of this informal workshop is to showcase work by the Texas A&M community on scientific machine learning and to foster networking and collaboration.
Open Postdoctoral Research Associate Position in Scientific Machine Learning
The SciML Lab is seeking a Postdoctoral Research Associate t the interface of artificial intelligence and scientific computation, developing physics-informed machine learning methods and applying them in various cutting-edge engineering and scientific projects with researchers from various related disciplines affiliated with TAMIDS.