129 citations · 261 across the 12 of their papers we have counts for
12 papers
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…
UQpy v4.1: Uncertainty Quantification with Python
Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis +9
This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured t…
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…
Grassmannian diffusion maps based surrogate modeling via geometric harmonics
Ketson R. M. dos Santos, Dimitrios G. Giovanis, Katiana Kontolati +2
In this paper, a novel surrogate model based on the Grassmannian diffusion maps (GDMaps) and utilizing geometric harmonics is developed for predicting the response of engineering s…
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…
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models
Katiana Kontolati, Dimitrios Loukrezis, Ketson R. M. dos Santos +2
In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to ide…
Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids
Katiana Kontolati, Darius Alix-Williams, Nicholas M. Boffi +3
We introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based o…
Numerical analysis of a mechanotransduction dynamical model reveals homoclinic bifurcations of extracellular matrix mediated oscillations of the mesenchymal stem cell fate
Katiana Kontolati, Constantinos Siettos
We perform one and two-parameter numerical bifurcation analysis of a mechanotransduction model approximating the dynamics of mesenchymal stem cell differentiation into neurons, adi…