Katiana Kontolati

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Michael D. ShieldsJohns Hopkins UniversityVerified email at jhu.edu
Somdatta GoswamiAssistant Professor, Civil and Systems Engineering, Johns Hopkins UniversityVerified email at jh.edu
George Em KarniadakisThe Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and EngineeringVerified email at brown.edu
Dimitris G GiovanisAssistant Research Professor, John Hopkins University, Civil EngineeringVerified email at jhu.edu
Dimitrios LoukrezisResearch scientist, Siemens AG | Research group leader, TU DarmstadtVerified email at temf.tu-darmstadt.de
Lohit VandanapuPhD Candidate, Johns Hopkins UniversityVerified email at jhu.edu
Ketson R M dos SantosAssistant Professor, University of Minnesota Twin CitiesVerified email at umn.edu
Michael L. FalkProfessor of Materials Science and Engineering, Johns Hopkins UniversityVerified email at jhu.edu
Nicholas M. BoffiCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Chris H. RycroftProfessor, University of Wisconsin–MadisonVerified email at math.wisc.edu
Audrey OlivierAssistant Professor of Civil and Environmental Engineering, USCVerified email at usc.edu
Nishant PandaInformation Sciences, CCS-3, Los Alamos National LabVerified email at lanl.gov
Natalie KleinLos Alamos National LaboratoryVerified email at lanl.gov
Diane OyenLos Alamos National LaboratoryVerified email at cs.unm.edu
Panagiotis (Panos) TsilifisGeneral Electric Vernova Advanced ResearchVerified email at ge.com
Valeria AndreoliAerodynamics Engineer, GE ResearchVerified email at ge.com
Sayan GhoshSenior Engineer at GE Aerospace ResearchVerified email at ge.com
Constantinos SiettosDepartment of Mathematics and Applications, University of Naples Federico IIVerified email at unina.it

Katiana Kontolati

PhD, Johns Hopkins University

Verified email at jhu.edu - Homepage

uncertainty quantification stochastic simulation scientific machine learning


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Deep transfer operator learning for partial differential equations under conditional shift S Goswami, K Kontolati, MD Shields, GE Karniadakis Nature Machine Intelligence 4 (12), 1155-1164, 2022	94*	2022
On the influence of over-parameterization in manifold based surrogates and deep neural operators K Kontolati, S Goswami, MD Shields, GE Karniadakis Journal of Computational Physics 479, 112008, 2023	38	2023
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems K Kontolati, D Loukrezis, DG Giovanis, L Vandanapu, MD Shields Journal of Computational Physics 464, 111313, 2022	38	2022
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models K Kontolati, D Loukrezis, KRM Dos Santos, DG Giovanis, MD Shields International Journal for Uncertainty Quantification 12 (4), 2022	35	2022
Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems K Kontolati, S Goswami, G Em Karniadakis, MD Shields Nature Communications 15 (1), 5101, 2024	21*	2024
Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids K Kontolati, D Alix-Williams, NM Boffi, ML Falk, CH Rycroft, MD Shields Acta Materialia 215, 117008, 2021	16	2021
Uqpy v4. 1: Uncertainty quantification with python D Tsapetis, MD Shields, DG Giovanis, A Olivier, L Novak, P Chakroborty, ... SoftwareX 24, 101561, 2023	9	2023
Grassmannian diffusion maps based surrogate modeling via geometric harmonics KRM dos Santos, DG Giovanis, K Kontolati, D Loukrezis, MD Shields International Journal for Numerical Methods in Engineering 123 (15), 3507-3529, 2022	8	2022
Numerical investigation of weak axis I profile connections K Kontolati, A Koukouselis, O Panagouli Master's thesis, University of Thessaly, Greece, 2018	8	2018
Multi-fidelity metamodeling in turbine blade airfoils via transfer learning on manifolds K Kontolati, P Tsilifis, S Ghosh, V Andreoli, M Shields, L Wang AIAA SCITECH 2023 Forum, 0918, 2023	2	2023
Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra K Kontolati, N Klein, N Panda, D Oyen arXiv preprint arXiv:2108.08709, 2021	2	2021
Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving V Kumar, S Goswami, K Kontolati, MD Shields, GE Karniadakis arXiv preprint arXiv:2408.02198, 2024		2024
Leveraging intrinsic model and data structures for predictive physics-based modeling and uncertainty quantification K Kontolati Johns Hopkins University, 2023		2023
Neural Density Estimation and Uncertainty Quantification for ChemCam Spectra [Slides] K Kontolati, N Panda, NE Klein, JS Moore, DA Oyen Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2021		2021
Multiscale Modeling of Plasticity in Amorphous Solids: Machine Learning Constitutive Response M Falk, D Alix-Williams, A Hinkle, D Giovanis, K Kontolati, C Rycroft, ... APS March Meeting Abstracts 2021, Y20. 001, 2021		2021
Numerical analysis of mesenchymal stem cell mechanotransduction dynamics reveals homoclinic bifurcations K Kontolati, C Siettos International Journal of Non-Linear Mechanics 113, 146-157, 2019		2019
Αριθμητική προσομοίωση συνδέσεων ασθενούς άξονα διατομών διπλού ταυ ΚΣ Κοντολάτη		2017

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