129 citations · 261 across the 12 of their papers we have counts for
7 papers · 1 filter
Biology-informed neural networks learn nonlinear representations from omics data to improve genomic prediction and interpretability
Katiana Kontolati, Rini Jasmine Gladstone, Ian Davis +1
We extend biologically-informed neural networks (BINNs) for genomic prediction (GP) and selection (GS) in crops by integrating thousands of single-nucleotide polymorphisms (SNPs) w…
Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving
Varun Kumar, Somdatta Goswami, Katiana Kontolati +2
Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-ta…
Learning in latent spaces improves the predictive accuracy of deep neural operators
Katiana Kontolati, Somdatta Goswami, George Em Karniadakis +1
Operator regression provides a powerful means of constructing discretization-invariant emulators for partial-differential equations (PDEs) describing physical systems. Neural opera…
Deep transfer operator learning for partial differential equations under conditional shift
Somdatta Goswami, Katiana Kontolati, Michael D. Shields +1
Transfer learning (TL) enables the transfer of knowledge gained in learning to perform one task (source) to a related but different task (target), hence addressing the expense of d…
On the influence of over-parameterization in manifold based surrogates and deep neural operators
Katiana Kontolati, Somdatta Goswami, Michael D. Shields +1
Constructing accurate and generalizable approximators for complex physico-chemical processes exhibiting highly non-smooth dynamics is challenging. In this work, we propose new deve…
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems
Katiana Kontolati, Dimitrios Loukrezis, Dimitris G. Giovanis +2
Constructing surrogate models for uncertainty quantification (UQ) on complex partial differential equations (PDEs) having inherently high-dimensional stoc…
Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra
Katiana Kontolati, Natalie Klein, Nishant Panda +1
Constructing probability densities for inference in high-dimensional spectral data is often intractable. In this work, we use normalizing flows on structured spectral latent spaces…