Alex Tartakovsky

Professor

UIUC

Affiliations

The Grainger College of Engineering

Civil & Environmental Engineering

Department of Civil and Environmental Engineering

Alex Tartakovsky is currently serving as a Civil & Environmental Engineering Professor at Uiuc.

Honors

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Experiences

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Education

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Alex Tartakovsky

UIUC

Professor

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applied mathematics fluid flow fluid dynamics numerical simulations fluid mechanics transport phenomena engineering porous materials computational fluid dynamics civil engineering dispersion science multiphase flows pore structure physics diffusion porous media porous body scientific computing numerical modeling (geology) machine learning particle methods porosity machine learning research particle-laden flows disperse flow fluid-solid interactions multiscale hydrodynamics neural network (machine learning) fluid science numerical mathematics computational intelligence hydrological process physic aware machine learning scalar transport hydraulic research bayesian analysis vadose zone uncertainty modeling boundary value problems diffusion process reactive transport numerical method for partial differential equation stochastic differential equations statistical physics pde-constrained optimization gaussian process smoothed particle hydrodynamics surface tension anomalous diffusion multiscale analysis multiscale phenomena stochastic calculus geological modeling computational geophysics recurrent neural networks semi-implicit method energy forecasting data assimilation reaction process gaussian process regression hydrodynamic stability stochastic phenomena physical modeling wetting sparse grids physics-informed neural network deep neural network physics-informed machine probability density function particle hydrodynamics model physics-informed neural networks multiphysics data assimilation fluid particles particle hydrodynamics friction stir welding multicomponent reactive transport subsurface flow uncertainty quantification multiscale modeling

Notable Scholarly Works

Notable Federal Grants

Collaborators

Karhunen–LoèVe Deep Learning Method for Surrogate Modeling and Approximate Bayesian Parameter Estimation. Advances in Water Resources (2025)

Yuanzhe WangYifei ZongJames L. McCreightJoseph D. HughesMichael FienenAlexandre M. Tartakovsky

deep learningstatistical inferencecomputational intelligencemodel optimizationparameter estimationbiostatisticsmachine learningbayesian analysisdata sciencesurrogate modelingstatistical modelsneural network (machine learning)

Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation. Computer Methods in Applied Mechanics and Engineering (2025)

Yifei ZongDavid Barajas-SolanoAlexandre M. Tartakovsky

computational intelligencedata assimilationmachine learningbayesian analysisdata sciencephysic aware machine learningneural computationbayesian data assimilationcomputational modelsneural network (machine learning)

Solving High-Dimensional Inverse Problems Using Amortized Likelihood-Free Inference With Noisy and Incomplete Data. Computer Methods in Applied Mechanics and Engineering (2025)

Jice ZengYuanzhe WangAlexandre M. TartakovskyDavid A. Barajas-Solano

amortized likelihood-free inferencestatistical inferencehigh-dimensional statisticsstatistical methodologyregularization (mathematics)high-dimensional methoddata sciencestatistical modelsapplied mathematicshigh-dimensional inverse problemsmathematical optimizationinverse problemsincomplete data

Total Uncertainty Quantification in Inverse Solutions With Deep Learning Surrogate Models. Journal of Computational Physics (2025)

Yuanzhe WangJames L. McCreightJoseph D. HughesAlexandre M. Tartakovsky

deep learningcomputational intelligenceuncertainty modelinguncertain datauncertainty analysismachine learningdata scienceuncertainty quantificationtotal uncertainty quantificationinverse solutionsdata surrogatemachine learning researchapplied mathematicsdeep uncertaintyinverse problems

A Deep Learning-Based Workflow for Fast Prediction of 3D State Variables in Geological Carbon Storage: A Dimension Reduction Approach. Journal of Hydrology (2024)

Hongsheng WangSeyyed A. HosseiniAlexandre M. TartakovskyJianqiao LengMing Fan

numerical modeling (geology)deep learningcarbon sequestrationgeoscientific model developmentgeological modelingstate variablesmachine learningcomputational geochemistryscientific computingdata sciencefast predictionearth sciencedeep learning-based workflow

Bayesian Reduced-Order Deep Learning Surrogate Model for Dynamic Systems Described by Partial Differential Equations. Computer Methods in Applied Mechanics and Engineering (2024)

Yuanzhe WangYifei ZongJames L. McCreightJoseph D. HughesAlexandre M. Tartakovsky

partial differential equationsnonlinear dynamicsnonlinear systemsbayesian analysisdata sciencedynamical systemsparameter identificationapplied mathematicspde-constrained optimizationdynamic systemsnonlinear analysisneural network (machine learning)reduced order modeling

Chance Constrained Load Frequency Control of Power Systems With Wind Resources. Journal of the Franklin Institute (2024)

Tong MaDavid Alonso Barajas-SolanoAlexandre M. Tartakovsky

power systemsenergy systemscontrol systemsload frequency controlcontrol optimizationpower deliverysystems engineeringwind power generationwind powerpower system controlwind resourcesload controlfrequency controlpower system dynamicscontrol system technologysmart grid

Comparison of Machine Learning and Electrical Resistivity Arrays to Inverse Modeling for Locating and Characterizing Subsurface Targets. Journal of Applied Geophysics (2024)

Jamil, AhsanRucker, Dale F.Lu, DanBrooks, Scott C.Tartakovsky, Alexandre M.Cao, HuipingCarroll, Kenneth C.

geophysical prospectingmachine learningsensor arrayssubsurface targetscomputer engineeringinverse scattering transformselectrical resistivity arraysmachine learning researchapplied mathematicsinverse problems

Comparison of ML-Based Proxy Modeling Strategies: Lessons Learned From the Smart Initiative. (2024)

Jared SchuetterChung ShihPaul HolcombHongkyu YoonMeen KadeethumAlex TartakovskyChristian OroSeyyed HosseiniHongsheng Wang

computer sciencesimulation modellingcarbon injectionsystems modelingintelligent computingcomputer modelingdigital earthsmart initiativedata sciencedesign automationsystems engineeringmodel integrationmodeling methoddata modelingsmart projectmodeling and simulation

Gaussian Process Regression and Conditional Karhunen-LoèVe Models for Data Assimilation in Inverse Problems. Journal of Computational Physics (2024)

Yu-Hong YeungDavid A. Barajas-SolanoAlexandre M. Tartakovsky

partial differential equationsnumerical simulationsexecution timeparameter estimationgaussian processscientific computingdata scienceuncertainty quantificationnumerical algorithmsgaussian process regressionapplied mathematicsnumerical analysisdiscretization nodesdata assimilationinverse problems