DOE OSTI · 3007466
MuSIKAL: Multiphysics Simulations and Knowledge Discovery Through AI/ML Technologies (Final Technical Report)
Abstract
Under the MuSiKAL project, we developed a framework for a coastal digital twin (DT) platform capable of integrating diverse data resources, configuring multiscale model simulations, performing SciML‐accelerated predictions, with applications primarily driven by storm surge and heavily rainfall events impacting the Gulf Coast of the U.S.
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Leung, Ruby [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)], Kaiser, Hartmut [Louisiana State Univ., Baton Rouge, LA (United States)], Kaiser, Carola [Louisiana State Univ., Baton Rouge, LA (United States)], Westerink, Joannes J. [University of Notre Dame, IN (United States)], Niyogi, Dev [Univ. of Texas, Austin, TX (United States)], Yang, Zong-LIang [Univ. of Texas, Austin, TX (United States)], Sun, Alex [Univ. of Texas, Austin, TX (United States)], Scanlon, Bridget [Univ. of Texas, Austin, TX (United States)], Bui-Thanh, Tan [Univ. of Texas, Austin, TX (United States)], Dawson, Clint [Univ. of Texas, Austin, TX (United States)] (ORCID:0000000172730684). 2025-12-11. MuSIKAL: Multiphysics Simulations and Knowledge Discovery Through AI/ML Technologies (Final Technical Report). https://doi.org/10.2172/3007466
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