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Found 15 papers, 3 papers with code
High-Precision Geosteering via Reinforcement Learning and Particle Filters
We integrate an RL-based geosteering with PF to address realistic geosteering scenarios.
Neural oscillators for magnetic hysteresis modeling
Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems.
Neural oscillators for generalization of physics-informed machine learning
A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented by partial differential equations (PDEs).
Learning Nonautonomous Systems via Dynamic Mode Decomposition
We present a data-driven learning approach for unknown nonautonomous dynamical systems with time-dependent inputs based on dynamic mode decomposition (DMD).
Discovery of sparse hysteresis models for piezoelectric materials
This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression techniques.
Feature-Informed Data Assimilation -- Definitions and Illustrative Examples
We introduce a mathematical formulation of feature-informed data assimilation (FIDA).
Machine Learning in Heterogeneous Porous Materials
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learning (ML) and applied mathematics to identify how ML can advance materials research.
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties
We use a convolutional adversarial autoencoder (CAAE) for the parameterization of the heterogeneous non-Gaussian conductivity field with a low-dimensional latent representation.
Transfer Learning on Multi-Fidelity Data
The former is reported relative to both CNN training on high-fidelity images only and Monte Carlo solution of the PDEs.
Autonomous learning of nonlocal stochastic neuron dynamics
When random excitations are modeled as Gaussian white noise, the joint PDF of neuron states satisfies exactly a Fokker-Planck equation.
Mutual Information for Explainable Deep Learning of Multiscale Systems
Timely completion of design cycles for complex systems ranging from consumer electronics to hypersonic vehicles relies on rapid simulation-based prototyping.
GINNs: Graph-Informed Neural Networks for Multiscale Physics
We introduce the concept of a Graph-Informed Neural Network (GINN), a hybrid approach combining deep learning with probabilistic graphical models (PGMs) that acts as a surrogate for physics-based representations of multiscale and multiphysics systems.
Dynamics of Data-driven Ambiguity Sets for Hyperbolic Conservation Laws with Uncertain Inputs
This study focuses on the latter step by investigating the spatio-temporal evolution of data-driven ambiguity sets and their associated guarantees when the random QoIs they describe obey hyperbolic partial-differential equations with random inputs.
Optimization and Control Analysis of PDEs
Data-Driven Discovery of Coarse-Grained Equations
Statistical (machine learning) tools for equation discovery require large amounts of data that are typically computer generated rather than experimentally observed.
Causality and Bayesian network PDEs for multiscale representations of porous media
The global sensitivity indices are used to rank the effect of uncertainty in microscopic parameters on macroscopic QoIs, to quantify the impact of causality on the multiscale model's predictions, and to provide physical interpretations of these results for hierarchical nanoporous materials.
