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Texas A&M Institute of Data Science

Scientific Machine Learning Lab

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.

TAMIDS SciML Lab Seminar Series: Chris Rackauckas: “Stiffness: Where Deep Learning Breaks and How Scientific Machine Learning Can Fix It”

Dr. Chris Rackauckas, Applied Mathematics Instructor at MIT, Director of Modeling and Simulation at Julia Computing, and the Director of Scientific Research at Pumas-AI, will present an online seminar in the TAMIDS Scientific Machine Learning (SciML) Lab Seminar Series: “Stiffness: Where Deep Learning Breaks and How Scientific Machine Learning Can Fix It” on Wednesday April 14th, 2021, 1-2pm CST. Dr. Rackauckas is a pioneer in the SciML area, whose open source DifferentialEquations.jl Julia software is widely used in academia and industry.

Hackathon on Material Design with Graph Learning

From April 19 to April 23, the Scientific Machine Learning Lab will organize a one-week-long Hackathon to explore potential applications of graphical learning in material design. Graph deep learning utilizes graph neural networks to learn and analyze graph data in various domains that include but not limited to social networks, traffic networks, natural science, knowledge graphs, and material design. In material design, graph generation has been demonstrated to be a very effective method in molecule discovery and material search.

Position: Postdoc in Scientific Machine Learning

The TAMIDS Scientific Machine Learning (SciML) Lab is part of a new initiative to develop knowledge, resources, and community around thematic areas of Data Science / Artificial Intelligence / Machine Learning, encompassing research, education, and outreach. TAMIDS is seeking to recruit a postdoctoral research associate to join the SciML Lab multidisciplinary team, currently comprising six faculty drawn from the Colleges of Science and Engineering, researchers from TAMIDS and Texas A&M High Performance Research Computing, and associated graduate students.

New Scientific Machine Learning Class

TAMIDS has made an award under its Data Science Course Development Program to SciML Lab Director Dr. Ulisses Braga-Neto to develop a new for-credit class in Scientific Machine Learning. This class, with stacked sections at the undergraduate and graduate levels, will be one of the first for-credit SciML courses in the United States.

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, …

TAMIDS SciML Lab Seminar Series: Paris Perdikaris: Bridging Physical Models and Observational Data with Physics-Informed Deep Learning

Dr. Paris Perdikaris, Assistant Professor in the Department of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania, will present an online talk in the TAMIDS Scientific Machine Learning (SciML) Lab Seminar Series: “Bridging Physical Models and Observational Data with Physics-Informed Deep Learning” on Wednesday March 10th, 2021, 1-2pm CST.

Tao Co-leads the Texas A&M 12th Unmanned Team

SciML Lab Associate Director Jian Tao co-led the “12th Unmanned Team” that ranked second overall in the the 2020 AutoDrive Challenge. The team was selected to compete in GM/SAE Autodrive Challenge II.

TAMIDS/TEES/HPRC SciML 2020 Workshop

The TAMIDS/TEES/HPRC workshop on Scientific Machine Learning was held in 27 October 2020. Workshop slides and videos are available at the workshop website.