The Workshop Schedule is now available.
With permission from the speakers, six recorded videos are now made available. The TAMIDS SciML youtube playlist is embedded below. Individual links to the videos can be found in the schedule section.
Scientific Machine Learning (SciML) is a rapidly developing area that aims to revolutionize the practice of Science and Engineering, by bringing together the fields of Machine Learning and Scientific Computation. Typical data-driven Machine Learning methodologies do not incorporate physical understanding of the problem domain. Furthermore, in many scientific domains high-fidelity data are expensive or time-consuming to obtain. Physics-aware SciML addresses both of these problems by introducing regularizing constraints obtained from physical laws, allowing prediction of future performance of complex multiscale, multiphysics systems using sparse, low-fidelity, and heterogeneous data. Unlike traditional black-box Machine Learning methods, SciML aims to deliver interpretable models, leading to improved verification and validation in mission-critical applications.
This informal workshop is intended to help identify and bring together members of the academic community at Texas A&M who are interested in studying SciML. Areas of interest include: physics-informed deep neural networks, 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: multifidelity 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 Synthetic Biology, the workshop encourages discussion of potential applications in other fields that may integrate domain knowledge and models with machine learning. Through the exchange of ideas and results we hope to foster coherent research efforts that can lead to new scientific advances and enhance competitiveness for external funding.
The workshop is open to all members of Texas A&M via zoom meeting ID 945 9928 7403 passcode 526915 (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 SciMl and devise strategies to strengthen Texas A&M’s ability to address them.
TAMU Scientific Machine Learning Mailing List
https://groups.google.com/a/lists.tamu.edu/g/sciml (select “Join Group”; requires login with TAMU NetID)
Each technical talk will comprise 15 minutes presentation followed by 10 minutes discussion
|8:30-8:35||Nick Duffield, TAMIDS & ECE, Welcome|
|8:35-8:45||Narasimha Reddy, TEES & ECE, Introduction|
|8:45-9:10||Lisa Perez, High Performance Research Computing|
Scientific Machine Learning Resources at Texas A&M’s High Performance Research Computing Facility
|9:10-9:35||Eduardo Gildin, Petroleum Engineering|
Scientific Machine Learning for Fast Reservoir Simulation and Prediction
|9:35-10:00||Lifan Wang, Physics and Astronomy|
Artificial Intelligence Assisted Inversion of Supernova Explosion Models
|10:00-10:25||Ulisses Braga-Neto, Electrical and Computer Engineering|
Self-Adaptive Physically-Informed Neural Networks with Applications in Microstructure Informatics
|10:25-10:50||Xia (Ben) Hu, Computer Science & Engineering, AutoML Systems in Action|
|11:00-11:45||Round table discussion.|
Technical challenges and steps to growing SciML as a coherent research thrust at Texas A&M
|11:45-12:10||Raktim Bhattacharya, Aerospace Engineering, |
A Convex Optimization Framework for Generating Finite Difference Schemes for Arbitrary PDEs (Discovered or Derived)
|12:10-12:35||Jiachen Ding, Atmospheric Sciences, Automatic Pixel-by-pixel Contrail Cloud Detections|
|12:35-1:00||Yalchin Efendiev, Mathematics, Multiscale Simulations and Machine Learning|
Background Literature and Online Resources
- Chris Rackauckus, The Essential Tools of Scientific Machine Learning (Scientific ML), Stochastic Lifestyle (Blog post), 2019
- Baker, Nathan, Alexander, Frank, Bremer, Timo, Hagberg, Aric, Kevrekidis, Yannis, Najm, Habib, Parashar, Manish, Patra, Abani, Sethian, James, Wild, Stefan, Willcox, Karen, & Lee, Steven. Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. https://www.osti.gov/servlets/purl/1478744
- Innes, M., Edelman, A., Fischer, K., Rackauckus, C., Saba, E., Shah, V. B., & Tebbutt, W. (2019). Zygote: A differentiable programming system to bridge machine learning and scientific computing. https://arxiv.org/abs/1907.07587
- Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686-707. https://doi.org/10.1016/j.jcp.2018.10.045
- Tijana Radivojević, Zak Costello, Kenneth Workman, Hector Garcia Martin, A machine learning Automated Recommendation Tool for synthetic biology, Nature Communications, Vol, 11, Article number: 4879 (2020), https://doi.org/10.1038/s41467-020-18008-4
- Jie Zhang, et. al., Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism, Nature Communications, Vol. 11, Article number: 4880 (2020), https://doi.org/10.1038/s41467-020-17910-1
Ms. Jennifer South, TAMIDS, email@example.com