Research Scientist at Stanford University, United States
Dr. Sorentav is a computational scientist specializing in energy science and engineering. With expertise in neural networks, physics-informed machine learning, and computational fluid dynamics, he has contributed significantly to advancing numerical modeling techniques. His research focuses on shock physics, subsurface flows, additive manufacturing, and uncertainty quantification. He has developed innovative computational frameworks for high-fidelity simulations and accelerated engineering applications. Dr. Sorentav has published in leading scientific journals, reviewed research papers, and supervised students and interns. His interdisciplinary approach bridges machine learning with physics-based simulations, enhancing predictive accuracy in various domains. He is proficient in multiple programming languages, including Python, C++, MATLAB, and OpenFOAM, and has a strong background in Unix/Linux environments. Through collaborations with academic institutions and industry, he has contributed to cutting-edge projects in materials science, energy systems, and computational mechanics.
Pofile
scholar
Education
Dr. Sorentav holds a Ph.D. in Computational Science from the University of California, San Diego (UCSD), where he developed novel numerical techniques for solving complex physics-informed problems in energy and material sciences. His doctoral research focused on advancing simulation accuracy for multiphysics systems, particularly in shock-particle interactions and uncertainty quantification. Prior to his Ph.D., he earned a Master’s degree in Computational Science from UCSD, specializing in physics-informed neural networks and high-performance computing. He also holds a Bachelor’s degree from Katholieke Universiteit Leuven, where he built a solid foundation in applied mathematics, fluid dynamics, and numerical modeling. Throughout his academic career, Dr. Sorentav has received multiple awards for research excellence, including recognition for his Ph.D. dissertation. His education has equipped him with expertise in Monte Carlo simulations, finite difference/volume methods, and applied probability, which he integrates into cutting-edge computational science applications.
Experience
Dr. Sorentav has extensive experience in computational modeling, numerical methods, and physics-informed machine learning. He has worked on developing and validating high-fidelity simulations for energy applications, materials science, and shock physics. His research contributions include designing neural network architectures for scientific computing, implementing uncertainty quantification methods, and improving computational efficiency in large-scale simulations. Dr. Sorentav has collaborated with leading institutions, including Stanford University and UCSD, to accelerate computational model development for industrial and research applications. He has also contributed to proposal writing, conference presentations, and peer-reviewed journal publications. His technical expertise spans various software tools, including PyTorch, OpenFOAM, MATLAB, FEniCS, and Mathematica. Additionally, he has experience supervising student research projects, mentoring interns, and leading interdisciplinary teams. His work integrates applied probability, numerical analysis, and machine learning to address challenges in subsurface flows, additive manufacturing, and compressible fluid dynamics.
Publications
Graph-Informed Neural Networks & Machine Learning in Multiscale Physics
Graph-informed neural networks (GINNs) for multiscale physics ([J. Comput. Phys., 2021, 33 citations])
Mutual information for explainable deep learning in multiscale systems ([J. Comput. Phys., 2021, 15 citations])
Machine-learning-based multi-scale modeling for shock-particle interactions ([Bulletin of the APS, 2019, 1 citation])
These papers focus on integrating neural networks into multiscale physics, leveraging explainability techniques, and improving shock-particle simulations through ML.
2. Monte Carlo Methods & Uncertainty Quantification
Estimation of distributions via multilevel Monte Carlo with stratified sampling ([J. Comput. Phys., 2020, 32 citations])
Accelerated multilevel Monte Carlo with kernel-based smoothing and Latinized stratification ([Water Resour. Res., 2020, 19 citations])
Impact of parametric uncertainty on energy deposition in irradiated brain tumors ([J. Comput. Phys., 2017, 4 citations])
This work revolves around Monte Carlo methods, uncertainty quantification, and their applications in medical physics and complex simulations.
3. Stochastic & Hybrid Models in Nonlinear Systems
Noise propagation in hybrid models of nonlinear systems ([J. Comput. Phys., 2014, 16 citations])
Conservative tightly-coupled stochastic simulations in multiscale systems ([J. Comput. Phys., 2016, 9 citations])
A tightly-coupled domain decomposition approach for stochastic multiphysics ([J. Comput. Phys., 2017, 8 citations])
This research contributes to computational physics, specifically in stochastic and hybrid system modeling.
4. Computational Fluid Dynamics (CFD) & Shock-Wave Interactions
Two-way coupled Cloud-In-Cell modeling for non-isothermal particle-laden flows ([J. Comput. Phys., 2019, 7 citations])
Multi-scale simulation of shock waves and particle clouds ([Int. Symp. Shock Waves, 2019, 1 citation])
Inverse asymptotic treatment for capturing discontinuities in fluid flows ([J. Comput. Sci., 2023, 2 citations])
S. Taverniers has significantly contributed to shock-wave interaction modeling, with applications in aerodynamics and particle-fluid interactions.
5. Computational Plasma & Dielectric Breakdown Modeling
2D particle-in-cell modeling of dielectric insulator breakdown ([IEEE Conf. Plasma Science, 2009, 11 citations])
This early work focuses on plasma physics and dielectric breakdown simulations.
6. Nozzle Flow & Additive Manufacturing Simulations
Finite element methods for microfluidic nozzle oscillations ([arXiv, 2023])
Accelerating part-scale simulations in liquid metal jet additive manufacturing ([arXiv, 2022])
Modeling of liquid-gas meniscus dynamics in arbitrary nozzle geometries (US Patent, 2024)
Conclusion
Based on their remarkable academic achievements, innovative research, and ability to collaborate effectively across disciplines, this candidate is highly deserving of the Best Researcher Award. However, by broadening their industrial collaborations, increasing their research visibility, and considering the wider impact of their work, they could elevate their research contributions even further, making an even greater impact on both academia and industry.