Search Results for author:
Found 7 papers, 3 papers with code
Improving Explainability of Softmax Classifiers Using a Prototype-Based Joint Embedding Method
We propose a prototype-based approach for improving explainability of softmax classifiers that provides an understandable prediction confidence, generated through stochastic sampling of prototypes, and demonstrates potential for out of distribution detection (OOD).
Explainable Artificial Intelligence (XAI)
Out-of-Distribution Detection
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement
Comprehensive experiments show that VDGN policies significantly outperform error threshold-based policies in global error and cost metrics.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning robust marking policies for adaptive mesh refinement
In this work, we revisit the marking decisions made in the standard adaptive finite element method (AFEM).
Learning the structure of wind: A data-driven nonlocal turbulence model for the atmospheric boundary layer
The DRD model can be calibrated with noisy data from field experiments.
Learning orbital dynamics of binary black hole systems from gravitational wave measurements
We introduce a gravitational waveform inversion strategy that discovers mechanical models of binary black hole (BBH) systems.
A priori error analysis of high-order LL* (FOSLL*) finite element methods
In turn, limited convergence rates appear because the regularity of this Lagrange multiplier is determined, in part, by the geometry of the domain.
Numerical Analysis Numerical Analysis 65N12, 65N15, 65N30
Adaptive sampling strategies for risk-averse stochastic optimization with constraints
This method is applicable to a broad class of expectation-based risk measures and leads to a significant reduction in the individual gradient evaluations used to estimate the objective function gradient.
Stochastic Optimization
Optimization and Control
