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Jun 1, 2026 (virtual) Plzen, Czechia

Khachik Sargsyan, Nikhil Iyengar, Daniel Ricciuto

PyTUQ - UQ Tools and Workflows for Computational Models

Mar 25, 2026 Minneapolis, MN

Khachik Sargsyan, Emilie Grace Eduria Baillo, Bert Debusschere

2025

PyTUQ - Python Toolkit for Uncertainty Quantification

Sep 8, 2025 virtual

Khachik Sargsyan

Inverse Modeling via Bayesian Inference

Apr 22, 2025 virtual

Khachik Sargsyan

Uncertainty Quantification in Computational Science -- from Physical Models to Neural Networks

Feb 17, 2025 Banff, Canada

Khachik Sargsyan

2024

Embedded Framework for Model Error Quantification and Propagation

Aug 14, 2024 Arlington, VA

Khachik Sargsyan

Spatio-Temporal Surrogate Construction and Calibration of E3SM Land Model

Jun 10, 2024 (virtual) Plzen, Czechia

Khachik Sargsyan, Daniel Ricciuto

Reduced-Dimensional Neural Network Surrogate Construction for the E3SM Land Model

May 6, 2024 (virtual) Livermore, CA

Khachik Sargsyan, Daniel Ricciuto

Uncertainty Quantification and Calibration of E3SM Land Model

Apr 24, 2024 Livermore, CA

Khachik Sargsyan, Daniel Ricciuto

Reduced-Dimensional Neural Network Surrogate Construction for the E3SM Land Model

Mar 1, 2024 Trieste, Italy

Khachik Sargsyan, Daniel Ricciuto

Quantifying Uncertainties in Weight-Parameterized Residual Neural Networks

Mar 1, 2024 Trieste, Italy

Khachik Sargsyan, Oscar Diaz-Ibarra, Javier Murgoitio-Esandi, Joshua Hudson, Marta D’Elia, Habib Najm

Reduced-Dimensional Neural Network Surrogate Construction and Calibration of the E3SM Land Model

Feb 15, 2024 virtual

Khachik Sargsyan, Daniel Ricciuto

2023

Reduced-Dimensional Neural Network Surrogate Construction and Calibration of the E3SM Land Model

Dec 11, 2023 San Francisco, CA

Khachik Sargsyan, Daniel Ricciuto

Uncertainty Quantification in Neural Networks

Nov 6, 2023 (virtual) Livermore, CA

Khachik Sargsyan

Visualizing and Quantifying Uncertainty of Physics-aware Neural Networks

Nov 2, 2023 Denver, CO

Khachik Sargsyan

Uncertainty Quantification and Parameter Calibration for High-Dimensional Output Fields of Earth System Models

Sep 13, 2023 Rockville, MD

Khachik Sargsyan, Luis Damiano, Maia Richards-Dinger, Bert Debusschere, Michael Eldred, Chih-Chieh-Jack Chen, Walter Hannah, James Benedict

Quantifying Uncertainties in Residual Neural Networks and Neural ODEs

Jun 12, 2023 Greece, Athens

Khachik Sargsyan, Joshua Hudson, Oscar Diaz-Ibarra, Marta D’Elia, Habib N. Najm

Model Error Estimation for Active Learning of Interatomic Potential Models

Mar 3, 2023 Amsterdam, Netherlands

Khachik Sargsyan, Logan Williams, Habib N. Najm

2022

Uncertainty Quantification of Machine Learning Interatomic Potential Models

Aug 19, 2022 Arlington, VA

Khachik Sargsyan, Logan Williams, Habib N. Najm

Training and Generalization of Residual Neural Networks as Discrete Analogues of Neural ODEs

Jul 26, 2022 (virtual) Albuquerque, NM

Khachik Sargsyan, Joshua Hudson, Marta D'Elia, Habib N. Najm

Model Error Estimation and Uncertainty Quantification of Machine Learning Interatomic Potentials

Jun 23, 2022 (virtual) Lausanne, Switzerland

Khachik Sargsyan, Logan Williams, Habib N. Najm

UQ and Model Error Estimation for Machine Learning Interatomic Potentials

Jun 6, 2022 (virtual) Livermore, CA

Khachik Sargsyan, Logan Williams, Habib N. Najm

Quantification and Propagation of Uncertainties in Machine Learning Interatomic Potentials for Molecular Dynamics

Apr 22, 2022 (virtual) Atlanta, GA

Khachik Sargsyan, Habib N. Najm

Bayesian Inference of Interatomic Potentials

Mar 24, 2022 (virtual) Boston, MA

Khachik Sargsyan

2021

Hit Twice by the Curse of Dimensionality; Spatio-Temporal Land Model Calibration using Karhunen-Loeve and Sparse Polynomial Chaos Expansions

Dec 13, 2021 (virtual) New Orleans, LA

Khachik Sargsyan, Daniel Ricciuto, Cosmin Safta

Active Learning and Uncertainty Quantification for Machine Learning Interatomic Potentials

Sep 28, 2021 (virtual) San Diego, CA

Khachik Sargsyan, Katherine Johnston, Varuni Dantanarayana, Habib N. Najm

Quantifying and Reducing Uncertainty in the E3SM Land Model using Surrogate Modeling

May 27, 2021 virtual

Daniel Ricciuto, Khachik Sargsyan, Peter Thornton, Dan Lu

Minima-preserving neural network (MPNN) for potential energy surface approximation

May 5, 2021 (virtual) Livermore

Khachik Sargsyan, Katrin Blondal, David Bross, Branko Ruscic, C. Franklin Goldsmith

Statistical Learning and Model Error Estimation

Apr 13, 2021 virtual

Khachik Sargsyan

Dimensionality Reduction and Physics-Informed Neural Networks for Climate Land Models

Mar 4, 2021 virtual

Khachik Sargsyan, Cosmin Safta, Vishagan Ratnaswamy, Daniel M. Ricciuto

2020

Dimensionality Reduction and Physics-Informed Recurrent Neural Networks for Climate Land Models

Dec 1, 2020 virtual

Khachik Sargsyan, Cosmin Safta, Daniel Ricciuto

Physics-informed Machine Learning for Uncertainty Quantification in E3SM Land Model

Oct 16, 2020 virtual

Khachik Sargsyan, Cosmin Safta, Daniel Ricciuto

Probabilistic Methods for Forward and Inverse Uncertainty Quantification

Oct 12, 2020 virtual

Khachik Sargsyan

Uncertainty Quantification with Model Structural Error

Sep 4, 2020 (virtual) Rome

Khachik Sargsyan

Physics-informed Machine Learning for Uncertainty Quantification in Land Models

Aug 5, 2020 virtual

Khachik Sargsyan, Cosmin Safta, Vishagan Ratnaswamy, Daniel Ricciuto

Embedded Model Error Quantification and Propagation in Physical Models

May 13, 2020 (virtual) Munich, Germany

Khachik Sargsyan, Tiernan Casey, Habib Najm

2019

Calibration and Propagation of Model Structural Error for E3SM Land Model

Dec 9, 2019 San Francisco, CA

Khachik Sargsyan, Cosmin Safta, Vishagan Ratnaswamy, Daniel Ricciuto

Surrogate-enabled Parameter Calibration and Structural Error Estimation in Land Models

Nov 20, 2019 Arlington, VA

Khachik Sargsyan

Active Machine Learning with Uncertainties and Gradients

Oct 28, 2019 Sandia National Laboratories, Livermore, CA

Khachik Sargsyan

Embedded Model Error Quantification and Propagation in Physical Systems

Aug 26, 2019 Sandia National Laboratories, Albuquerque, NM

Khachik Sargsyan

Surrogate-enabled Sensitivity Analysis and Parameter Inference of High-Dimensional Models

Jul 19, 2019 Valencia, Spain

Khachik Sargsyan, Cosmin Safta

Uncertainty Quantification and Propagation for Impurity Migration

Jul 16, 2019 Rockville, MD

Khachik Sargsyan, Timothy Younkin, Tiernan Casey, Habib Najm, Brian Wirth

Active Learning for Linearly Parameterized Interatomic Potentials

May 21, 2019 Sandia National Laboratories, Livermore, CA

Khachik Sargsyan

Bayesian Inference for Structural Error Quantification

Feb 26, 2019 Spokane, WA

Khachik Sargsyan, Xun Huan, Habib N. Najm

2018

Bayesian Framework for Structural Uncertainty Estimation of Land Models

Dec 10, 2018 Washington, DC

Khachik Sargsyan, Daniel Ricciuto, Cosmin Safta

Overview of Uncertainty Quantification Methods for Complex Models

Nov 6, 2018 Potomac, MD

Khachik Sargsyan, Cosmin Safta, Daniel Ricciuto

Overview of Uncertainty Quantification Methods for Complex Models

Nov 6, 2018 Potomac, MD

Khachik Sargsyan, Cosmin Safta, Daniel Ricciuto

Machine Learning for Potential Energy Surface Approximation

Oct 23, 2018 Sandia National Laboratories, Livermore, CA

Khachik Sargsyan

Machine Learning for PES Approximation

Oct 3, 2018 Sandia National Laboratories, Livermore, CA

Khachik Sargsyan

Bayesian Framework for Embedded Model Error Representation and Quantification

Jul 30, 2018 Vancouver, Canada

Khachik Sargsyan, Xun Huan, Habib N. Najm

Embedded Model Error Representation in Computational Models

Jul 25, 2018 New York, NY

Khachik Sargsyan, Xun Huan, Habib N. Najm

Bayesian Inference for Model Error Quantification and Propagation with UQTk

Jul 13, 2018 Portland, OR

Khachik Sargsyan, Xun Huan, Habib Najm, Cosmin Safta, Kenny Chowdhary, Bert Debusschere

Embedded Model Error Representation for Bayesian Model Calibration

May 31, 2018 Radcliffe Institute for Advanced Study, Cambridge, MA

Khachik Sargsyan, Xun Huan, Habib N. Najm

UQ Update - Fitting Models to Langmuir Probe Data

May 23, 2018 virtual

Khachik Sargsyan, Tiernan Casey, Habib Najm, Timothy Younkin

Embedded Model Error Quantification and Propagation

Apr 19, 2018 Anaheim, CA

Khachik Sargsyan, Xun Huan, Habib N. Najm

Probabilistic Methods for Uncertainty Quantification in Computational Models

Apr 6, 2018 Ann Arbor, MI

Khachik Sargsyan

2017

Embedded Model Error Representation and Propagation in Climate Models

Dec 12, 2017 New Orleans, LA

Khachik Sargsyan, Daniel Ricciuto, Cosmin Safta, Peter Thornton

Structural Error Quantification in Physical Models

Sep 21, 2017 Sandia National Laboratories, Livermore, CA

Khachik Sargsyan

Forward and Inverse Uncertainty Quantification for ALM Single Point Model

Jun 6, 2017 Potomac, MD

Khachik Sargsyan, Daniel Ricciuto

Model Error Quantification in Turbulent Combustion Computations

Feb 27, 2017 Atlanta, GA

Khachik Sargsyan, Xun Huan, Zachary Vane, Guilhem Lacaze, Joe Oefelein, Habib Najm

Probabilistic Numerics

Feb 9, 2017 Sandia National Laboratories, Livermore, CA

Khachik Sargsyan

Embedded Model Error Representation

Jan 30, 2017 Livermore, CA

Khachik Sargsyan, Xun Huan, Habib Najm

2016

Weighted Iterative Bayesian Compressive Sensing (WIBCS) for High Dimensional Polynomial Surrogate Construction

Dec 14, 2016 San Francisco, CA

Khachik Sargsyan, Daniel Ricciuto, Cosmin Safta, Bert Debusschere, Habib Najm, Peter Thornton

Parametric Uncertainty Quantification and Dimensionality Reduction for ALM at FLUXNET sites

Nov 10, 2016 Denver, CO

Khachik Sargsyan, Daniel Ricciuto

Density Estimation Framework for Model Error Quantification

Jul 11, 2016 Boston, MA

Khachik Sargsyan, Habib Najm

Density Estimation Framework for Model Error Estimation

Jun 13, 2016 Sardinia, Italy

Khachik Sargsyan, Habib Najm, Jason Bender, Chi Feng, Youssef Marzouk

Quantifying the Impacts of Parametric Uncertainty on Biogeochemistry in the ACME Land Model

Jun 7, 2016 Rockville, MD

Khachik Sargsyan, Daniel Ricciuto

Probabilistic Methods for Uncertainty Quantification in Computational Models I

Apr 19, 2016 virtual

Khachik Sargsyan

Density Estimation Framework for Model Error Quantification

Apr 7, 2016 Lausanne, Switzerland

Khachik Sargsyan, Habib N. Najm, Jason Bender, Chi Feng, Youssef Marzouk

Quantifying the Impacts of Parametric Uncertainty on Biogeochemistry in the ACME Land Model

Apr 5, 2016 Lausanne, Switzerland

Khachik Sargsyan, Daniel Ricciuto, Peter Thornton

Model Error Quantification

Feb 29, 2016 Sandia National Laboratories, Livermore, CA

Khachik Sargsyan

2015

Sparse Polynomial Chaos Surrogate for ACME Land Model via Iterative Bayesian Compressive Sensing

Dec 18, 2015 San Francisco, CA

Khachik Sargsyan, Daniel Ricciuto, Peter Thornton

Parametric Uncertainty Quantification Workflow for ALM

Nov 5, 2015 Albuquerque, NM

Khachik Sargsyan, Daniel Ricciuto, Peter Thornton

Parametric Uncertainty Analysis for ACME Land Model

Nov 5, 2015 Albuquerque, NM

Khachik Sargsyan, Daniel Ricciuto, Peter Thornton

Density Estimation Framework for Model Error Assessment

Jul 29, 2015 San Diego, CA

Khachik Sargsyan, Jason Bender, Habib Najm, Roger Ghanem

High-Dimensional Sparse Surrogate Construction via Bayesian Compressive Sensing

May 27, 2015 Crete Island, Greece

Khachik Sargsyan, Cosmin Safta, Habib N. Najm, Bert Debusschere, Daniel Ricciuto, Peter Thornton

High-Dimensional Surrogate Models and Global Sensitivity Analysis

May 5, 2015 Tysons Corner, VA

Khachik Sargsyan, Cosmin Safta, Habib Najm, Bert Debusschere, Daniel Ricciuto, Peter Thornton

High-Dimensional Sparse Surrogate Construction via Bayesian Compressive Sensing

Apr 29, 2015 Buenos Aires, Argentina

Khachik Sargsyan, Cosmin Safta, Habib N. Najm, Bert Debusschere, Daniel Ricciuto, Peter Thornton

Fast Evaluation of Many-Body Perturbation Integrals

Apr 2, 2015 Los Angeles, CA

Khachik Sargsyan, Prashant Rai, Habib Najm, S. Yoo Willow, Matthiew Hermes, So Hirata

Bayesian Compressive Sensing Framework for High-Dimensional Surrogate Construction

Mar 18, 2015 Salt Lake City, UT

Khachik Sargsyan, Cosmin Safta, Daniel Ricciuto, Bert Debusschere, Habib Najm, Peter Thornton

2014

Parameter Estimation in High-Dimensional Climate Models

Dec 15, 2014 San Francisco, CA

Khachik Sargsyan, Cosmin Safta, Bert Debusschere, Habib Najm, Dan Ricciuto, Peter Thornton

Density Estimation Framework for Model Error Assessment I

Dec 15, 2014 San Francisco, CA

Khachik Sargsyan, Zhen Liu, Habib Najm, Cosmin Safta, Bart van Bloemen Waanders, Hope Michelsen, Ray Bambha

Bayesian Compressive Sensing Framework for High-Dimensional Surrogate Model Construction

Jun 27, 2014 Salt Lake City, UT

Khachik Sargsyan, Cosmin Safta, Bert Debusschere, Habib Najm

Bayesian Compressive Sensing Framework for Sparse Representations of High-Dimensional Models

Apr 1, 2014 Savannah, GA

Khachik Sargsyan, Cosmin Safta, Bert Debusschere, Habib N. Najm

2013

Uncertainty Quantification in High-Dimensional, Nonlinear and Expensive Models

Nov 21, 2013 Livermore, CA

Khachik Sargsyan

Bayesian Compressive Sensing and Dimensionality Reduction for High-Dimensional Models

Nov 21, 2013 Lawrence Berkeley National Laboratory, Berkeley, CA

Khachik Sargsyan

High-dimensional Polynomial Chaos Basis Selection with Bayesian Compressive Sensing

Jul 24, 2013 Raleigh, NC

Khachik Sargsyan, Cosmin Safta, Daniel Ricciuto, Bert Debusschere, Habib Najm, Peter Thornton

Sparse Surrogate Model Construction via Compressive Sensing for High-Dimensional Complex Models

Jul 11, 2013 San Diego, CA

Khachik Sargsyan, Cosmin Safta, Bert Debusschere, Habib Najm

High-dimensional Polynomial Chaos Basis Selection with Bayesian Compressive Sensing

Feb 28, 2013 Boston, MA

Khachik Sargsyan, Cosmin Safta, Daniel Ricciuto, Bert Debusschere, Habib Najm, Peter Thornton

Surrogate Construction via Bayesian Compressive Sensing for the Community Land Model

Feb 20, 2013 Boulder, CO

Khachik Sargsyan, Cosmin Safta, Bert Debusschere, Habib Najm, Daniel Ricciuto, Peter Thornton

2012

Surrogate-Based Uncertainty Quantification in Climate Models in presence of High Dimensional, Dependent Inputs and Nonsmooth Outputs

Dec 5, 2012 San Francisco, CA

Khachik Sargsyan, Cosmin Safta, Jaideep Ray, Bert Debusschere, Habib Najm, Daniel Ricciuto, Peter Thornton

Adaptive Basis Selection and Dimensionality Reduction with Bayesian Compressive Sensing

Apr 3, 2012 Raleigh, NC

Khachik Sargsyan

UQ Challenges and Techniques to Address Them

Feb 23, 2012 Berkeley, CA

Khachik Sargsyan

2011

Uncertainty Quantification in Climate Modeling

Dec 2, 2011 San Francisco, CA

Khachik Sargsyan, Cosmin Safta, Robert Berry, Bert Debusschere, Habib Najm, Daniel Ricciuto, Peter Thornton

Predictability Assessment in Stochastic Reaction Networks

Mar 7, 2011 Los Angeles, CA

Khachik Sargsyan, Bert Debusschere, Habib Najm

Predictability in Stochastic Reaction Networks

Feb 24, 2011 Reno, NV

Khachik Sargsyan, Bert Debusschere, Habib Najm

2010

Uncertainty Quantification given Discontinuous Climate Model Response and a Limited Number of Model Runs

Dec 13, 2010 San Francisco, CA

Khachik Sargsyan, Cosmin Safta, Bert Debusschere, Habib Najm

Uncertainty Quantification in Climate Modeling

Jul 20, 2010 Baltimore, MD

Khachik Sargsyan, Cosmin Safta, Bert Debusschere, Habib Najm

Uncertainty Quantification Methodologies for Climate Model Data with Discontinuities

Jul 14, 2010 Pittsburgh, PA

Khachik Sargsyan, Cosmin Safta, Bert Debusschere, Habib Najm

Advanced Tools for Uncertainty Quantification in Climate Modeling

Jun 28, 2010 Breckenridge, CO

Khachik Sargsyan, Cosmin Safta, Bert Debusschere, Habib Najm

Bayesian Methods for Discontinuity Detection in Climate Model Predictions

Jun 3, 2010 Benidorm, Spain

Khachik Sargsyan, Cosmin Safta, Bert Debusschere, Habib Najm

Uncertainty Quantification and Data Assimilation - Applications in Stochastic Reaction Networks and Climate Modeling

Feb 24, 2010 Sandia National Laboratories, Livermore, CA

Khachik Sargsyan

2009

Uncertainty Quantification and Data Assimilation - Applications in Chemical Kinetics and Climate Modeling

Nov 23, 2009 Sandia National Laboratories, Livermore, CA

Khachik Sargsyan

Reduced Order Modeling and Dynamical Analysis in Stochastic Reaction Networks

Mar 2, 2009 Miami, FL

Khachik Sargsyan, Bert Debusschere, Habib Najm

2008

Predictability and Reduced Order Modeling in Stochastic Reaction Networks

Jul 7, 2008 San Diego, CA

Khachik Sargsyan, Bert Debusschere, Habib Najm, Youssef Marzouk