15 citations · 21 across the 11 of their papers we have counts for
11 papers
GeoQ: Geometry-Aware Conditional Quantile Error Estimation for Scientific Surrogate Models
Khoa Nguyen, Daniel Serino, Aviral Prakash +1
Neural-network surrogate models are increasingly used to accelerate scientific simulations, but their deployment in extrapolative and autoregressive settings requires input-depende…
Revealing Low-Dimensional Structure in 2D Richtmyer-Meshkov Instabilities via Parametric Reduced-Order Modeling
Daniel Messenger, Daniel Serino, Balu Nadiga +1
Efficient modeling of the Richtmyer-Meshkov instability (RMI) is essential to many engineering tasks, including high-speed combustion and drive and capsule geometry optimization in…
Learning robust parameter inference and density reconstruction in flyer plate impact experiments
Evan Bell, Daniel A. Serino, Ben S. Southworth +2
Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, espec…
Structure-Preserving Neural Ordinary Differential Equations for Stiff Systems
Allen Alvarez Loya, Daniel A. Serino, J. W. Burby +1
Neural ordinary differential equations (NODEs) are an effective approach for data-driven modeling of dynamical systems arising from simulations and experiments. One of the major sh…
Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions
Daniel A. Serino, Evan Bell, Marc Klasky +4
In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of th…
Reconstructing Richtmyer-Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks
Daniel A. Serino, Marc L. Klasky, Balasubramanya T. Nadiga +2
A trained attention-based transformer network can robustly recover the complex topologies given by the Richtmyer-Meshkoff instability from a sequence of hydrodynamic features deriv…
An adaptive Newton-based free-boundary Grad-Shafranov solver
Daniel A. Serino, Qi Tang, Xian-Zhu Tang +2
Equilibria in magnetic confinement devices result from force balancing between the Lorentz force and the plasma pressure gradient. In an axisymmetric configuration like a tokamak,…
Neural Network Representations of Multiphase Equations of State
George A. Kevrekidis, Daniel A. Serino, Alexander Kaltenborn +3
Equations of State model relations between thermodynamic variables and are ubiquitous in scientific modelling, appearing in modern day applications ranging from Astrophysics to Cli…
Fast-Slow Neural Networks for Learning Singularly Perturbed Dynamical Systems
Daniel A. Serino, Allen Alvarez Loya, Joshua W. Burby +2
Singularly perturbed dynamical systems play a crucial role in climate dynamics and plasma physics. A powerful and well-known tool to address these systems is the Fenichel normal fo…
A stable added-mass partitioned (AMP) algorithm for elastic solids and incompressible flow
Daniel A. Serino, Jeffrey W. Banks, William D. Henshaw +1
A stable added-mass partitioned (AMP) algorithm is developed for fluid-structure interaction (FSI) problems involving viscous incompressible flow and compressible elastic solids. D…
A stable added-mass partitioned (AMP) algorithm for elastic solids and incompressible flow: model problem analysis
Daniel A. Serino, Jeffrey W. Banks, William D. Henshaw +1
A stable added-mass partitioned (AMP) algorithm is developed for fluid-structure interaction (FSI) problems involving viscous incompressible flow and compressible elastic-solids. T…