
Surrogate modeling & model reduction
Cross-
Cutting
Research Area
Harnessing the data through the lens of physics-based modeling
Scientific Machine Learning brings together the complementary perspectives of computational science and computer science to craft a new generation of machine learning methods for complex applications across science and engineering. In these applications, dynamics are complex and multiscale, data are sparse and expensive to acquire, decisions have high consequence, and uncertainty quantification is essential. The greatest challenges facing society — clean energy, climate change, sustainable urban infrastructure, access to clean water, personalized medicine and more — by their very nature require predictions that go well beyond the available data. Scientific machine learning achieves this by incorporating the predictive power, interpretability and domain knowledge of physics-based models.
Research is multifaceted, ranging from foundational advances in theory, methods and algorithms, to real-world impact in societal grand challenge problems.
Surrogate modeling & model reduction
Bayesian inverse problems
Physics-informed deep learning
Data assimilation
Interpretable machine learning
Reinforcement learning
Digital twins
Optimal experimental design
The Oden Institute and The Alan Turing Institute have a memorandum of understanding to collaborate in the areas of artificial intelligence for science and engineering, computational science and engineering, scientific machine learning, and data-centric engineering. Established in 2015, The Alan Turing Institute is a high-profile, vibrant and multidisciplinary national institute, bringing together 13 leading universities from England and Scotland, making it the UK’s national institute for data science and artificial intelligence.
The Oden Institute's Center for Scientific Machine Learning has strong collaborations with Department of Energy programs, including the AEOLUS Multifaceted Mathematics Integrated Capability Center, the ARPA-E DIFFERENTIATE program for design intelligence, and the Artificial Intelligence and Decision Support for Complex Systems program. Our faculty also played a key role in the ASCR visioning report on Basic Research Needs for Scientific Machine Learning
.To learn more about projects and people in Scientific Machine Learning, explore the centers and groups with research activities in this cross-cutting research area.
Center for Scientific Machine Learning
Optimization, Inversion, Machine Learning, and Uncertainty for Complex Systems
Computational Research in Ice and Ocean Systems Group
Probabilistic and High Order Inference, Computation, Estimation, and Simulation
News
Feb. 27, 2023
Researchers from UT Austin have been awarded a prestigious grant from the UK’s Wellcome Trust to understand how flood and storm surge modeling could help inform infectious disease spread in Texas and beyond.
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Feb. 9, 2023
The new simulation took only 9.4 milliseconds to run on a laptop computer, one billion times faster than a traditional model.
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Sept. 26, 2022
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Sept. 12, 2022
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