Minisymposia

The conference will feature five themes organized in 10 minisymposia (two per theme). Minisymposia are by invitation only. 

Below, we list the themes and the minisymposia organizers of the two, separate sessions associated with each theme.
Speakers and abstracts will be posted together with the final program.

Theme 1: Mathematical analysis

• MS1.1, Analysis of Fluid Dynamics by Jon Wilkening (UC Berkeley, LBNL)
  Thursday, 10:30am-12:00pm, room CC105.

  • Pascale Garaud, UC Santa Cruz, Multiscale asymptotic analysis for strongly stratified turbulent flows.

  • Ahmad Abassi, UC Berkeley, Semi-analytic theory and algorithms for finite-depth standing water waves.

  • Sameer Iyer, UC Davis, Recent Progress on Inviscid Damping and Enhanced Dissipation in the Presence of Boundaries.

  • Anuj Kumar, UC Berkeley, Optimal scalar and vector transport using branching flows.

• MS1.2, Advanced Numerical Methods and their Applications by Mayya Tokman and Andy Wan (UC Merced)
  Wednesday, 3:00-4:30pm, room CC110.

  • Youngsoo Choi, LLNL, Train Small, Model Big: A Novel Data-Driven Scaleup for Large-Scale Physics Simulations.

  • Mario Bencomo, CSU Fresno, Physics-based preconditioner for iterative estimations of multipole sources. 

  • Tanya Tafolla, UCMerced, Low-synchronization Arnoldi Methods with Application to Exponential Integrators.

  • Andy Wan, UC Merced, Minimal l2 Norm Discrete Multiplier Method.

Theme 2: Optimization, inverse problems, and experimental design

• MS2.1, Mathematical Optimization and its Applications, by Robert Bassett (Naval Postgraduate School).
  Thursday, 10:30am-12:00pm, room CC110.

  • Robert Bassett, Naval Postgraduate School, Why Does Optimization Matter to the US Navy?

  • Johannes Royset, University of Southern California, Variational Analysis: The Mathematics of Optimization and Beyond.

  • Krishnakumar Balasubramanian, University of California Davis, Stochastic Optimization Algorithms for Instrumental Variable Regression. 

  • Jean Paul Watson, Lawrence Livermore National Laboratory, Recent Developments in the MPI-SPPY Solver Library for Scalable Stochastic Programming.

• MS2.2, Optimization, Inverse Problems, and Experimental Design, by Roummel Marcia (UC Merced) and Chrysoula Tsogka (UC Merced).
  Wednesday, 3:00-4:30pm, room CC110.

  • Arnold Kim, UC Merced,  Spectral classification of dispersive scatterers below a random rough air-soil interface.

  • Symeon Papadimitropoulos, UC Merced, Synthetic aperture radar imaging using convolutional neural networks.

  • Alexei Novikov, Pennsylvania State University, Dictionary learning for imaging in complex media.

  • Albert Fannjiang, UC Davis, Noise-robust 3D tomographic phase retrieval with one bit measurement.

Theme 3: Scientific and high performance computing

• MS3.1, Scientific and High Performance Computing Software and Education, by Cody Balos (LLNL)
  Thursday, 1:00pm-2:30pm, room CC105.

  • David Gardner, LLNL, An Overview of SUNDIALS Deployment in Scientific Application Codes. 

  • Edoardo Zoni, LBNL, Scientific Software Development for Beam, Plasma & Accelerator Modeling.

  • Francois Gygi, UC Davis, Adapting to Changing Computer Architectures: the Example of the Qbox First-Principles Molecular Dynamics Code.

  • Vivek Pallipuram, University of the Pacific, No Supercomputer? No Problem! A story of how a primarily undergraduate university promotes HPC education 

• MS3.2, Numerical Methods for Large-scale Scientific Computing, by Chao Yang (LBNL)
  Thursday, 3:00pm-4:30pm, room CC105.

  • Zhen Huang, UC Berkeley, Simulating non-Markovian open quantum systems through quasi-Lindblad pseudomode theory. 

  • Erika Ye, Lawrence Berkeley National Laboratory, Solving the Vlasov-Maxwell equations with quantized tensor trains.

  • Junhui Shen, UC Davis,  Fair PCA: Achieving Equity via Eigenvalue Optimization  

  • Hardeep Bassi, UC Merced, Ground state energy estimation from noisy quantum observables 

Theme 4: Uncertainty quantification and prediction

• MS4.1, Uncertainty Quantification and Prediction, by Habib Najm (SNL), Cosmin Safta (SNL), and Noemi Petra (UC Merced)
  Thursday, 1:00pm-2:30pm, room CC110.

  • Ying Cui, UC Berkeley, Optimization with Superquantile Constraints: A Fast Computational Approach 

  • Tony Zahtila, Stanford University, Multifidelity UQ for Laser-Ignition Reliability Analysis.

  • Noemi Petra, UC Merced, Exploiting Low-Dimensional Structure in Bayesian Inverse Problems Governed by PDEs. 

  • Christophe Bonneville, SNL, Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone.

• MS4.2, Data Assimilation for Uncertainty Reduction, by Daniel M. Tartakovsky (Stanford University), Wei Kang (Naval Postgraduate School), Daniele Venturi (UC Santa Cruz)
  Friday, 10:30am-12:00pm, room CC105.

  • Chiping Li, AFOSR, Challenges and Opportunities of Data Assimilation for Air Force Applications.

  • Wei Kang, Naval Postgraduate School, On the observability of the quantity of interest in data assimilation.

  •  Apoorv Srivastava, Stanford University,  Feature-Informed Data Assimilation: Making sense of binary-sensor observation.

  • Shu-Hua Chen, UC Davis,  Effects of assimilating aerosol observations on dust and low-level cloud forecasts over North Africa and the Atlantic Ocean.

Theme 5: Scientific machine learning, AI and digital twins

• MS5.1, AI-Powered Computational Physics: From Data to Physics-Consistent Predictions, by Shima Alizadeh (Amazon)
  Friday, 10:30am-12:00pm, room CC110.

  • Ashesh Chattopadhyay, UC Santa Cruz, Principled structures in deep learning-based autoregressive models of dynamical systems 

  • Walter Alvarado, NASA Ames Research Center, Understanding Chromatin Remodeling through Physics-Based Machine Learning Approaches

  • Cetin C. Kiris, Volcano Platforms Inc., Utilization of Large Eddy Simulations in Industrial Research and Development

  • Jordan B. Angel, Volcano Platforms Inc., High-Fidelity CFD Dataset for Machine Learning in Automotive Aerodynamics

• MS5.2, Physics-guided data-driven approach for physical simulations, by Youngsoo Choi (LLNL)
  Thursday, 3:00pm-4:30pm, room CC110.

  • Canceled: Charbel Farhat, Stanford University, Nonlinear Projection-Based Model Order Reduction Based on Modeling Closure Error in the Latent Space

    • New: Andy Wan, UC Merced, Compositional Physics Informed Neural Network

  • Elizabeth Krath, Sandia National Laboratories, Data-driven reduced-order modeling at Sandia: Workflows and Examples

  • Aditi Krishnapriyan, UC Berkeley, Bridging numerical methods and deep learning with physics-constrained differentiable solvers

  • Paul Tranquilli, LLNL, Building a parametric surrogate for shock-induced pore collapse with dynamic mode decomposition