115 citations · 131 across the 20 of their papers we have counts for
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An Agentic AI Scientific Community for Automated Neural Operator Discovery
Luis Loo, Ulisses Braga-Neto
We present an agentic approach to autonomous neural operator discovery based on an AI scientific community, which consists of a swarm of virtual laboratories that interact under a…
Free-RBF-KAN: Kolmogorov-Arnold Networks with Adaptive Radial Basis Functions for Efficient Function Learning
Shao-Ting Chiu, Siu Wun Cheung, Ulisses Braga-Neto +2
Kolmogorov-Arnold Networks (KANs) offer a promising framework for approximating complex nonlinear functions, yet the original B-spline formulation suffers from significant computat…
Convolution Operator Network for Forward and Inverse Problems (FI-Conv): Application to Plasma Turbulence Simulations
Xingzhuo Chen, Anthony Poole, Ionut-Gabriel Farcas +2
We propose the Convolutional Operator Network for Forward and Inverse Problems (FI-Conv), a framework capable of predicting system evolution and estimating parameters in complex sp…
In-Context Multi-Operator Learning with DeepOSets
Shao-Ting Chiu, Aditya Nambiar, Ali Syed +2
An important application of neural networks to scientific computing has been the learning of non-linear operators. In this framework, a neural network is trained to fit a non-linea…
BumpNet: A Sparse MLP Framework for Learning PDE Solutions
Shao-Ting Chiu, Ioannis G. Kevrekidis, Ulisses Braga-Neto
We introduce BumpNet, a sparse multilayer perceptron (MLP) framework for PDE numerical solution and operator learning. BumpNet is based on basis function expansion, which makes the…
A State-Space Approach to Nonstationary Discriminant Analysis
Shuilian Xie, Mahdi Imani, Edward R. Dougherty +1
Classical discriminant analysis assumes identically distributed training data, yet in many applications observations are collected over time and the class-conditional distributions…