computational research

This area involves study and research on theoretical and implementational aspects of numerical simulations: applied mathematics (functional analysis, partial differential equations, dynamical systems), numerical analysis (a- priori and a-posteriori error estimation, adaptive algorithms, stochasticity), computer science (high performance linear algebra, parallel computing), software engineering (programming in Fortran 90, C, C++ , data structures),visualization and geometry modeling, and mathematical modeling (multiscale, multiphysics problems).

Applications span across all disciplines of mechanics and related coupled, multiphysics problems: computational solid mechanics (fractures, phase transitions, plasticity, pattern formation), computational fluid mechanics and transport, semiconductor modeling, subsurface (multiphase flow in porous media) and surface flows, environmental modeling and remediation, computational wave propagation (elastodynamics, acoustics, electromagnetics), bioinformatics and bioengineering, computational material science. The program is closely related to graduate programs in Engineering Mechanics and the interdisciplinary programs in Computational and Applied Mathematics and Computational Engineering Sciences.

harvey storm surge simulation

Hurricane Harvey: Predicting the Storm Surge and Planning for Future Disasters

Learn how Texas ASE/EM researchers, in conjunction with UT Austin’s Center for Space Research, put their work to practice when Harvey hit the coast and their plans to mitigate future storms.

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tan bui vis lab

Minimizing Uncertainty in Defense, Energy

Assistant Professor Tan Bui-Thanh has received three new research grants to tackle the challenge of quantifying the uncertainty in the solution of large-scale data-driven inverse problems.

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Mary Wheeler research image

Mary Wheeler Awarded $1.5 Million NSF Grant to Develop Fracturing Simulation

Professor Mary Wheeler has received a $1.5 million grant from the National Science Foundation to develop computational techniques that more effectively use big data to predict and model the pathways of naturally-occurring ground fractures and how induced fractures interact with them.

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