Search Results for author:
Found 9 papers, 2 papers with code
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations.
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture
We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture.
Learning a Visually Grounded Memory Assistant
We introduce a novel interface for large scale collection of human memory and assistance.
CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations
We represent this reduced manifold using continuously differentiable neural fields, which may train on any and all available numerical solutions of the continuous system, even when they are obtained using diverse methods or discretizations.
Model reduction for the material point method via an implicit neural representation of the deformation map
Our technique approximates the $\textit{kinematics}$ by approximating the deformation map using an implicit neural representation that restricts deformation trajectories to reside on a low-dimensional manifold.
Optimal Assistance for Object-Rearrangement Tasks in Augmented Reality
We introduce a novel framework for computing and displaying AR assistance that consists of (1) associating an optimal action sequence with the policy of an embodied agent and (2) presenting this sequence to the user as suggestions in the AR system's heads-up display.
Deep Conservation: A latent dynamics model for exact satisfaction of physical conservation laws
In contrast to existing methods for latent dynamics learning, this is the only method that both employs a nonlinear embedding and computes dynamics for the latent state that guarantee the satisfaction of prescribed physical properties.
Computational Physics
Statistical closure modeling for reduced-order models of stationary systems by the ROMES method
Rather than target these two types of errors, this work proposes to construct a statistical model for the state error itself; it achieves this by constructing statistical models for the generalized coordinates characterizing both the in-plane error (i. e., the error in the trial subspace) and a low-dimensional approximation of the out-of-plane error.
Numerical Analysis
The ROMES method for statistical modeling of reduced-order-model error
To model normed errors, the method employs existing rigorous error bounds and residual norms as indicators; numerical experiments show that the method leads to a near-optimal expected effectivity in contrast to typical error bounds.
