Tan Bui-Thanh

About

Dr. Tan Bui-Thanh is a professor and the endowed William J. Murray Jr. Fellow in Engineering No. 4 in the Department of Aerospace Engineering and Engineering Mechanics (ASE/EM) and the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin. He obtained his Ph.D. from the Massachusetts Institute of Technology in 2007. Since joining the ASE/EM department in 2013, he has developed extensive expertise in multidisciplinary research across the boundaries of different branches of computational science, engineering, and mathematics. His research interests include quantum-accelerated Scientific Machine Learning (SciML) algorithms for digital twins, model-constrained Scientific Deep Learning (SciDL) algorithms, inverse problems, uncertainty quantification, numerical analysis, numerical optimization, and reduced-order modeling. Dr. Bui-Thanh has contributed significantly to PDE-constrained inverse problems, Bayesian inverse problems, and high-order finite element methods, and has developed physics-aware SciML approaches for computational sciences, engineering, and mathematics. He has published numerous works on real-time forecast and calibration (inversion) SciML algorithms that are deployable for digital twin applications, and is currently working on quantum-accelerated SciML algorithms for digital twins. He is a co-director of the Center for Scientific Machine Learning at the Oden Institute and has held leadership roles such as vice president…

Selected publications

• Simulation of the 3D hyperelastic behavior of ventricular myocardium using a finite-element based neural-network approach

  Computer Methods in Applied Mechanics and Engineering · 2022-04-01 · 28 citations

  articleOpen access
  PublisherOA PDFDOI

• Isogeometric finite element‐based simulation of the aortic heart valve: Integration of neural network structural material model and structural tensor fiber architecture representations

  International Journal for Numerical Methods in Biomedical Engineering · 2021-01-19 · 24 citations

  articleOpen access

  The functional complexity of native and replacement aortic heart valves (AVs) is well known, incorporating such physical phenomenons as time-varying non-linear anisotropic soft tissue mechanical behavior, geometric non-linearity, complex multi-surface time varying contact, and fluid-structure interactions to name a few. It is thus clear that computational simulations are critical in understanding AV function and for the rational basis for design of their replacements. However, such approaches co…

  PublisherOA PDFDOI

• <tt>TNet</tt>: A Model-Constrained Tikhonov Network Approach for Inverse Problems

  SIAM Journal on Scientific Computing · 2024-01-30 · 13 citations

  articleOpen accessSenior authorCorresponding

  Deep learning (DL), in particular deep neural networks, by default is purely data-driven and in general does not require physics. This is the strength of DL but also one of its key limitations when applied to science and engineering problems in which underlying physical properties—such as stability, conservation, and positivity—and accuracy are required. DL methods in their original forms are often not capable of respecting the underlying mathematical models or achieving desired accuracy even in…

  PublisherDOI

• Multi-patch epidemic models with partial mobility, residency, and demography

  Chaos Solitons & Fractals · 2023-06-28 · 10 citations

  article
  PublisherDOI

• Model-Constrained Deep Learning Approaches for Inverse Problems.

  arXiv (Cornell University) · 2021-05-25 · 6 citations

  preprintOpen accessSenior author

  Deep Learning (DL), in particular deep neural networks (DNN), by design is purely data-driven and in general does not require physics. This is the strength of DL but also one of its key limitations when applied to science and engineering problems in which underlying physical properties (such as stability, conservation, and positivity) and desired accuracy need to be achieved. DL methods in their original forms are not capable of respecting the underlying mathematical models or achieving desired…

  Publisher

Awards & honors

• William J Murray Jr. Fellow in Engineering No. 4
• NSF (OAC/DMS) early CAREER award
• Oden Institute distinguished research award
• Moncrief Grand Challenge award (two-time winner)
• Gordon Bell Prize finalist

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