This informal workshop is intended to showcase some of the current work being done by members of the Texas A&M community on cutting-edge scientific and engineering problems using physics-informed machine learning. Through the exchange of ideas and results, the workshop also aims to foster coherent research efforts that can lead to new scientific advances and enhance competitiveness for external funding. Areas of interest include physics-informed deep neural networks, physics-informed Gaussian processes, data-driven model discovery through large-scale simulation, ML-guided acceleration of numerical simulations; ML-guided automation of data acquisition and decision-support for complex systems; incorporating approaches such as multi-fidelity surrogate modeling, uncertainty quantification, Bayesian inference; and computational frameworks, systems, and methods for SciML. While recent advances in SciML have been driven primarily by applications in engineering, physical sciences, and systems biology, the workshop encourages discussion of potential applications in other fields that may integrate domain knowledge and models with machine learning.
The workshop is open to all members of Texas A&M virtually via Zoom (TAMU authentication required). No registration is required to attend. The workshop will comprise short invited talks from Texas A&M speakers with plenty of time for technical discussion, and a round table discussion to identify challenges and opportunities for collaborative work in SciML at Texas A&M.
TAMU Scientific Machine Learning Mailing List
https://groups.google.com/a/lists.tamu.edu/g/sciml (select “Join Group”; requires login with TAMU NetID)