activity
20062026
most citedCoarse-grained computations of demixing in dense gas-fluidized beds

6 citations · 9 across the 12 of their papers we have counts for

collaborators

16 papers

cs.LG2026

Generative Learning of Separatrices

Ellis R. Crabtree, Dimitris G. Giovanis, Anastasia Georgiou +2

The identification and reconstruction of the boundaries separating basins of attraction in multistable, multidimensional dynamical systems presents a fundamental challenge in compu…

math.OC2026

Singularities in Multi-Objective Optimization and their Crossing during Continuation

Arjun Manoj, Michail E. Kavousanakis, Shanqing Liu +1

Continuation methods help trace Pareto sets in multi-objective optimization but are inherently local: a single run traces a single connected branch, requiring multiple restarts to…

cs.LG2025

A Mechanistic Analysis of Transformers for Dynamical Systems

Gregory Duthé, Nikolaos Evangelou, Wei Liu +2

Transformers are increasingly adopted for modeling and forecasting time-series, yet their internal mechanisms remain poorly understood from a dynamical systems perspective. In cont…

cs.LG2025

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…

cs.LG2025

A Physics-informed Multi-resolution Neural Operator

Sumanta Roy, Bahador Bahmani, Ioannis G. Kevrekidis +1

The predictive accuracy of operator learning frameworks depends on the quality and quantity of available training data (input-output function pairs), often requiring substantial am…

math.NA2025

Neural network-based singularity detection and applications

Nadiia Derevianko, Ioannis G. Kevrekidis, Felix Dietrich

We present a method for constructing a special type of shallow neural network that learns univariate meromorphic functions with pole-type singularities. Our method is based on usin…