Honors & Awards
Nomination for the Chancellor’s Dissertation Medal, Department of Mechanical and Aerospace Engineering, University of California San Diego (2017)
Doctor of Philosophy, University of California San Diego, Engineering Physics (2016)
Master of Science, University of California San Diego, Engineering Physics (2012)
Master of Science, Catholic University of Leuven (KU Leuven), Physics (specialization in Nuclear and Radiation Physics) (2006)
Current Research and Scholarly Interests
Design and implementation of novel statistical algorithms based on the Multilevel Monte Carlo method to accelerate the quantification of uncertainty in quantities of interest for multiphase systems such as reactive granular media and subsurface flows.
Development of neural-network based surrogate approaches to enable data-driven sensitivity analysis and uncertainty quantification for multiscale systems such as energy storage systems, and accelerate the design process of such devices.
Daniel Tartakovsky, Data-Driven Modeling and Simulations Group (9/10/2018)
- Two-way coupled Cloud-In-Cell modeling of non-isothermal particle-laden flows: A Subgrid Particle-Averaged Reynolds Stress-Equivalent (SPARSE) formulation JOURNAL OF COMPUTATIONAL PHYSICS 2019; 390: 595–618
- Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor JOURNAL OF COMPUTATIONAL PHYSICS 2017; 348: 139–50
- A tightly-coupled domain-decomposition approach for highly nonlinear stochastic multiphysics systems JOURNAL OF COMPUTATIONAL PHYSICS 2017; 330: 884-901
- Conservative tightly-coupled simulations of stochastic multiscale systems JOURNAL OF COMPUTATIONAL PHYSICS 2016; 313: 400-414
Physics-based statistical learning approach to mesoscopic model selection
PHYSICAL REVIEW E
2015; 92 (5): 053301
In materials science and many other research areas, models are frequently inferred without considering their generalization to unseen data. We apply statistical learning using cross-validation to obtain an optimally predictive coarse-grained description of a two-dimensional kinetic nearest-neighbor Ising model with Glauber dynamics (GD) based on the stochastic Ginzburg-Landau equation (sGLE). The latter is learned from GD "training" data using a log-likelihood analysis, and its predictive ability for various complexities of the model is tested on GD "test" data independent of the data used to train the model on. Using two different error metrics, we perform a detailed analysis of the error between magnetization time trajectories simulated using the learned sGLE coarse-grained description and those obtained using the GD model. We show that both for equilibrium and out-of-equilibrium GD training trajectories, the standard phenomenological description using a quartic free energy does not always yield the most predictive coarse-grained model. Moreover, increasing the amount of training data can shift the optimal model complexity to higher values. Our results are promising in that they pave the way for the use of statistical learning as a general tool for materials modeling and discovery.
View details for DOI 10.1103/PhysRevE.92.053301
View details for Web of Science ID 000364412900020
View details for PubMedID 26651810
- Noise propagation in hybrid models of nonlinear systems: The Ginzburg-Landau equation JOURNAL OF COMPUTATIONAL PHYSICS 2014; 262: 313-324