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UID:submissions.pasc-conference.org_PASC22_sess172_pap122@linklings.com
SUMMARY:An Effective Physics Simulation Methodology Based on a Data-Driven
  Learning Algorithm
DESCRIPTION:Paper\n\nAn Effective Physics Simulation Methodology Based on 
 a Data-Driven Learning Algorithm\n\nJiang, Veresko, Liu, Cheng\n\nA method
 ology of multi-dimensional physics simulations for engineering and scienti
 fic applications is investigated based on a data-driven learning algorithm
  derived from proper orthogonal decomposition (POD). The approach utilizes
  numerical simulation tools to collect solution data for the problems of i
 nterest subjected to parametric variations that may include interior excit
 ations and/or boundary conditions influenced by exterior environments. The
  POD is applied to process the data and to generate a finite set of basis 
 functions. The problem is then projected from the physical domain onto a m
 athematical space constituted by its basis functions. The effectiveness of
  the POD methodology thus depends on the data quality, which relies on the
  numerical settings implemented in the data collection (or the <em>trainin
 g</em>). The simulation methodology is developed and demonstrated in a dyn
 amic heat transfer problem for an entire CPU and in a quantum eigenvalue p
 roblem for a quantum-dot structure. Encouraging findings are observed for 
 the POD simulation methodology in this investigation, including its extrem
 e efficiency, high accuracy and great adaptability. The models constructed
  by the POD basis functions are even capable of predicting the solution of
  the problem beyond the conditions implemented in the training with a good
  accuracy.\n\nDomain: Climate, Weather and Earth Sciences, Physics
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