News

Dr. Jian Tao joined the Department of Visualization in the College of Architecture on Sept 1st, 2021. Although he will continue working with engineering researchers and students, this website won’t be updated anymore. Please feel free to contact Jian at jtao@tamu.edu if you have any questions. Thanks!

https://sciml.tamids.tamu.edu/

Scientific Machine Learning Lab
TAMIDS has created a new program to collaborate with faculty teams in establishing Labs in emerging areas of Data Science. The mission of each Lab will be to develop knowledge, resources, and community around a thematic area of Data Science, encompassing research, education, and outreach. TAMIDS will support each Lab through a combination of seed funding for new research, effort from TAMIDS personnel, preferred access to existing TAMIDS programs, and organizational and logistical support. TAMIDS is piloting this program through its Scientific Machine Learning Lab (SciML Lab).

Scientific Machine Learning
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.

SciML Lab Goals
There is a great unexplored potential for research and education in Scientific Machine Learning, with many opportunities for high-impact, highly-visible research and extra-mural funding over the next decade. Texas A&M University, with its large research enterprise in engineering, science, and high-performance computing, is uniquely positioned to emerge as one of the national leaders in this area. The purpose of SciML Lab is to help to make this a reality. The lab will foster community building efforts by means of workshops and a seminar series, will develop new educational resources for SciML, including tutorials, hands-on short courses, and a for-credit courses with stacked sections at the undergraduate and graduate levels, all of which will be aimed at audiences drawn from multiple disciplines within Science, Engineering, Geosciences and Agriculture and also promote knowledge of SciML in further disciplines that present opportunities for impact.

 

https://tamids.tamu.edu/msds/

The Master of Science in Data Science degree is an on-campus interdisciplinary program offered by the Departments of Computer Science and Engineering, Electrical and Computer Engineering, Mathematics, and Statistics within the University’s Colleges of Engineering and Science, and administered jointly with the Texas A&M Institute of Data Science.

Application Deadline for Fall 2021: March 31

Eligibility Requirements: Applicants will need to have certain course work prior to enrolling in the program:

Math: calculus and linear algebra
Statistics: college level introduction to statistics
Some programming experience in at least one of languages: R, Python, C++.
Applicants with a bachelor’s degree in mathematics, statistics, computer science, electrical engineering, industrial engineering or similar fields should have the sufficient background.

October 27, 2020
8:30 am – 1:00 pm
Online via zoom

https://tamids.tamu.edu/2020/10/07/wkshp-sciml/

Scientific Background

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.

Workshop Aims

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.

Workshop Organization

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:

1. Short invited talks from Texas A&M speakers with plenty of time for technical discussion.
2. A round table discussion to identify challenges and opportunities for SciMl and devise strategies to strengthen Texas A&M’s ability to address them
3. Contributed slide presentations that will appear on the workshop website but not be delivered as talks at the workshop event.

Currently confirmed speakers are listed below. The full program will be announced during the week prior to the workshop.

Contributed Slide Presentations

Members of Texas A&M who wish to contribute a short slide presentation to appear on the workshop website (up to 5 slides, pdf format) conformant to the scope of the workshop should send these by email to Ms. Jennifer South jsouth@tamu.edu with the subject line “SciML Workshop Contributed Presentation”, preferably by end October 20, 2020.

Organizing Committee

Ulisses Braga-Neto (ECE), Nick Duffield (TAMIDS / ECE), Jian Tao (TEES / TAMIDS / HPRC / ECE), with thanks to Narasimha Reddy (ECE / TEES) for fostering discussion on Scientific Machine Learning.

Sponsoring Organizations

The Texas A&M Institute of Data Science (TAMIDS), the Texas A&M Engineering Experiment Station (TEES), Texas A&M High Performance Research Computing (HPRC)

Confirmed Speakers

• Raktim Bhattacharya, Aerospace Engineering, A Convex Optimization Framework for Generating Finite Difference Schemes for Arbitrary PDEs (Discovered or Derived)
• Ulisses Braga-Neto, Electrical and Computer Engineering, Self-Adaptive Physically-Informed Neural Networks with Applications in Microstructure Informatics
• Jiachen Ding, Atmospheric Sciences, Automatic Pixel-by-pixel Contrail Cloud Detections
• Yalchin Efendiev, Mathematics, Multiscale Simulations and Machine Learning
• Eduardo Gildin, Petroleum Engineering, Scientific Machine Learning for Fast Reservoir Simulation and Prediction
• Xia (Ben) Hu, Computer Science & Engineering, AutoML Systems in Action
• Lisa Perez, High Performance Research Computing, TBA
• Narasimha Reddy, Electrical and Computer Engineering and TEES, Introduction
• Lifan Wang, Physics and Astronomy, Artificial Intelligence Assisted Inversion of Supernova Explosion Models

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

Contact Information

Ms. Jennifer South, TAMIDS, jsouth@tamu.edu

 

TAMIDS Tech Talk on Tuesday, November 10th , by Vinit Sehgal, Ph.D. student in the Water Management and Hydrological Science Program at Texas A&M University.

More about the talk can be found at https://tamids.tamu.edu/2020/10/17/tamids-tech-talk-vinit-sehgal-large-scale-geospatial-analysis-with-r/

 

 

Mathworks and Texas A&M High Performance Research Computing are offering two introductory online sessions about data analysis and parallel computing with Matlab. These sessions are offered free of charge and are available to anyone at Texas A&M. Registration is required.

Wed October 28 | 1PM-3PM | WebEx session
Parallel Computing with MATLAB
Prerequisites: basic MATLAB knowledge

This session will discuss a number of features of the MATLAB Parallel Toolbox to speedup computations. This session will use live demonstrations and examples.

 

Wed October 28 | 3PM-5PM | WebEx session
Data Analysis with Matlab
Prerequisites: None

This session will use live demonstrations and examples to discuss how Matlab can be used to perform data analysis.

To see the complete catalog of short courses offered by High Performance Research Computing, please visit hprc.tamu.edu/training.

Weekly Online Bring-Your-Own-Data (BYOD) Workshops
Aug  26 – Dec 16, 2020
10:00 am – 11:30 am

Wednesdays online meeting via Zoom

The Texas A&M Institute of Data Science (TAMIDS), Texas A&M High Performance Research Computing (HPRC), and Texas Engineering Experiment Station invite you to join our Bring-Your-Own-Data (BYOD) workshop series!

These are one-on-one consultancy sessions with a TAMIDS Data Scientist who can help with formulating approaches to your Data Science research project, and assist with code development to take advantage of the latest data analytics methods and high-performance computing facilities.

This is a FREE service offered to all researchers at Texas A&M.

Sign up at Eventbrite (https://tinyurl.com/y2cjkbuz)