[HTML][HTML] Deepokan: Deep operator network based on kolmogorov arnold networks for mechanics problems
The modern digital engineering design often requires costly repeated simulations for
different scenarios. The prediction capability of neural networks (NNs) makes them suitable …
different scenarios. The prediction capability of neural networks (NNs) makes them suitable …
Synergistic learning with multi-task deeponet for efficient pde problem solving
Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful
information from multiple tasks to improve generalization performance compared to single …
information from multiple tasks to improve generalization performance compared to single …
Basis-to-basis operator learning using function encoders
Abstract We present Basis-to-Basis (B2B) operator learning, a novel approach for learning
operators on Hilbert spaces of functions based on the foundational ideas of function …
operators on Hilbert spaces of functions based on the foundational ideas of function …
Kolmogorov-Arnold PointNet: Deep learning for prediction of fluid fields on irregular geometries
A Kashefi - ar** between infinite-dimensional function spaces …
A Spectral-based Physics-informed Finite Operator Learning for Prediction of Mechanical Behavior of Microstructures
A novel physics-informed operator learning technique based on spectral methods is
introduced to model the complex behavior of heterogeneous materials. The Lippmann …
introduced to model the complex behavior of heterogeneous materials. The Lippmann …
Neural fields for rapid aircraft aerodynamics simulations
This paper presents a methodology to learn surrogate models of steady state fluid dynamics
simulations on meshed domains, based on Implicit Neural Representations (INRs). The …
simulations on meshed domains, based on Implicit Neural Representations (INRs). The …
[HTML][HTML] Tutorials: Physics-informed machine learning methods of computing 1D phase-field models
Phase-field models are widely used to describe phase transitions and interface evolution in
various scientific disciplines. In this Tutorial, we present two neural network methods for …
various scientific disciplines. In this Tutorial, we present two neural network methods for …