126 citations · 403 across the 35 of their papers we have counts for
15 papers · 1 filter
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 in scientific computing is non-linear operator learning. In this framework, a neural network is trained to fit a non-linear map between…
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…
Generalized Resubstitution for Regression Error Estimation
Diego Marcondes, Ulisses Braga-Neto
We propose generalized resubstitution error estimators for regression, a broad family of estimators, each corresponding to a choice of empirical probability measures and loss funct…
DeepOSets: Non-Autoregressive In-Context Learning with Permutation-Invariance Inductive Bias
Shao-Ting Chiu, Junyuan Hong, Ulisses Braga-Neto
In-context learning (ICL) is the remarkable ability displayed by some machine learning models to learn from examples provided in a user prompt without any model parameter updates.…
Label Propagation for Physics-Informed Neural Networks and Physics-Informed Gaussian Processes
Ming Zhong, Dehao Liu, Raymundo Arroyave +1
We present a series of empirical results of the application of semi-supervised label propagation techniques in training physics-informed machine learning methods. This includes sel…
Characteristics-Informed Neural Networks for Forward and Inverse Hyperbolic Problems
Ulisses Braga-Neto
We propose characteristics-informed neural networks (CINN), a simple and efficient machine learning approach for solving forward and inverse problems involving hyperbolic PDEs. Lik…
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture
Yicheng Wang, Xiaotian Han, Chia-Yuan Chang +3
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bringing together the power of deep learning to bear on scientific computation. In…
PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization
Caio Davi, Ulisses Braga-Neto
Physics-informed neural networks (PINN) have recently emerged as a promising application of deep learning in a wide range of engineering and scientific problems based on partial di…
Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCI
Parisa Ghane, Narges Zarnaghi Naghsh, Ulisses Braga-Neto
Motor imagery brain computer interface designs are considered difficult due to limitations in subject-specific data collection and calibration, as well as demanding system adaptati…
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
Levi McClenny, Ulisses Braga-Neto
Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equati…
A Semi-Supervised Generative Adversarial Network for Prediction of Genetic Disease Outcomes
Caio Davi, Ulisses Braga-Neto
For most diseases, building large databases of labeled genetic data is an expensive and time-demanding task. To address this, we introduce genetic Generative Adversarial Networks (…