TAMIDS SciML Lab 2021 Workshop
TAMIDS SciML Lab is hosting the 2nd workshop on Scientific Machine Learning on Oct 26. 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.
Updated: TAMIDS SciML Lab Workshop: TensorDiffEq for Efficient and Scalable Physics-Informed Deep Learning
TensorDiffEq is a software package designed and developed by members of the TAMU SciML community to implement collocation-based neuralPDE solvers, data assimilation solvers, as well as parameter inference and PDE discovery. This workshop aimed to promote scientific machine learning methods within the A&M research community and get more A&M researchers started in this exciting field. Meanwhile, we are interested in engaging a local community in the future development of TensorDiffEq.
TAMIDS SciML Lab Seminar Series: Sanjay Choudhry: NVIDIA SimNet: A Multi-Physics Neural Solver
Dr. Sanjay Choudhry is a Senior Director at NVIDIA and leads development in AI-driven Scientific Computing & Engineering. In this seminar, Choudhry will present his team’s work on NVIDIA SimNet, a multi-physics neural solver.
Update: Discovery Hackathon on Materials Design with Graph Learning
From April 19 to April 23, 2021, the TAMIDS SciML Lab organized a one-week-long Hackathon to explore potential applications of graph learning in materiasl design. The team consisted of two faculty members from the Department of Materials Science and Engineering, one research scientist from TAMIDS, and 8 graduate students drawn from the Department of Materials Science and Engineering, Chemistry, and Electrical and Computer Engineering. Together, they were able to modify and deploy the MatErials Graph Network (MEGNet) model, one of the best libraries of graph learning in the material science field, on Grace, the new supercomputer at Texas A&M.
HPRC Short Course: Introduction to Scientific Machine Learning
HPRC SciML Short Course (Registration Link Inside) Instructor: Jian Tao Location: Zoom session only (registration required) Prerequisites: Julia, basic understanding of partial differential equations and numerical methods. Scientific Machine Learning (SciML) is an emerging area that brings together the fields of Machine Learning and Scientific Computation. SciML introduces scientific model constraints in Machine Learning algorithms, …