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DTSTART;TZID=Europe/Stockholm:20240605T113000
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UID:submissions.pasc-conference.org_PASC24_sess153@linklings.com
SUMMARY:MS6H - Scalable Optimal Control and Learning Algorithms in High Pe
 rformance Computing
DESCRIPTION:Minisymposium\n\nLarge scale scientific computing is increasin
 gly pervasive in many research fields and industrial applications, such as
 : biological modeling of coupled soft-tissue systems, computational fluid 
 dynamics and turbulence, and tsunami inundation via shallow water equation
 s. Not only can forward simulation of these problems be prohibitively expe
 nsive, but additional computation and storage of gradient or adjoint infor
 mation further compounds such numerical burden. This matter becomes furthe
 r complicated when conducting an inverse or optimal control problem, which
  requires many such forward simulations and can suffer from the curse of d
 imensionality. Optimization and control problems can alleviate this burden
  somewhat via reduced-order surrogate models, randomized compression techn
 iques, neural network surrogates designed to imitate dynamics or operators
 . The goal of this minisymposium is to bring together researchers working 
 on finite- and infinite-dimensional control and simulation to discuss new 
 methodologies for analyzing and solving problems in the extreme computing 
 regime. It focuses on new techniques in model reduction for high-fidelity 
 physics simulations; surrogate modeling techniques based on learning; dist
 ributed and multilevel optimization on HPCs; compression and storage with 
 both deterministic and randomized methods; nonsmooth optimization; and ada
 ptive discretizations.\n\nLow-Rank PINNs for Model Reduction of Nonlinear 
 Hyperbolic Conservation Laws\n\nModel reduction for hyperbolic PDEs using 
 classical techniques is difficult due to the slow decay in the Kolmogorov 
 n-width, making it necessary to explore new forms of approximation. We wil
 l discuss a new approach using deep neural networks endowed with a particu
 lar low-rank structure, which we cal...\n\n\nRandall LeVeque (University o
 f Washington), Donsub Rim (Washington University in St. Louis), and Gerrit
  Welper (University of Central Florida)\n---------------------\nHermite Ke
 rnel Surrogates for the Value Function of  High-Dimensional  Nonlinear Opt
 imal Control Problems\n\nNumerical methods for the optimal feedback contro
 l of high-dimensional dynamical systems typically suffer from the curse of
  dimensionality. We devise a mesh-free data-based approximation method for
  the value-function for high dimensional optimal control problems, which p
 artially mitigates the dimens...\n\n\nTobias Ehring (University of Stuttga
 rt)\n---------------------\nAdaptive Randomized Sketching for Dynamic Nons
 mooth Optimization\n\nDynamic optimization problems arise in many applicat
 ions, such as optimal flow control, full waveform inversion, and medical i
 maging. Despite their ubiquity, such problems are plagued by significant c
 omputational challenges. For example, memory is often a limiting factor wh
 en determining if a proble...\n\n\nRobert Baraldi and Drew Kouri (Sandia N
 ational Laboratories) and Harbir Antil (George Mason University)\n--------
 -------------\nAdaptive ROM Methods in Optimal Design and Control\n\nROM i
 s already utilized successfully in optimization and control. Based on trus
 t-region methods new adaptive strategies for reduced basis schemes are int
 roduced. As numerical examples parameter estimation and optimization are c
 onsidered. The presented results are joint works with B. Azmi, B. Kaltenb.
 ..\n\n\nStefan Volkwein (University of Konstanz)\n\nDomain: Climate, Weath
 er, and Earth Sciences, Engineering, Life Sciences, Computational Methods 
 and Applied Mathematics\n\nSession Chair: Robert Baraldi (Sandia National 
 Laboratories)
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