جست‌وجوی استاد و پژوهشگر

Koji Ohkitaniمشاهده پروفایل

استاد

• Fluid Mechanics
• Vortex Dynamics
• Turbulence Theory
• +۴ مورد دیگر

Koji Ohkitani is a Professor at the Research Institute for Mathematical Sciences (RIMS), Kyoto University, specializing in fluid mechanics with a focus on vortex dynamics and turbulence theory. His work employs mathematical analysis, numerical computations, and physical modeling to study fundamental aspects of fluid behavior. His research interests center on: Vortex dynamics as a foundation for turbulence theory Self-similar solutions to the Navier-Stokes equations Comparison of fluid behaviors in different domains (whole space vs. periodic) Modified Navier-Stokes equations with depleted advection Interpolation between Navier-Stokes and Burgers equations Mathematical transforms (Cole-Hopf) for fluid systems Analysis of his recent publications reveals consistent themes in turbulence modeling, self-similar solutions, and numerical methods for fluid equations. His work frequently bridges theoretical mathematics with computational fluid dynamics, exploring singularity formation, scaling laws, and novel solution techniques across various fluid systems.

• Computational Statistics
• Machine Learning
• Applied Probability
• +۷ مورد دیگر

O. Deniz Akyildiz is an Assistant Professor in Statistics at the Department of Mathematics, Imperial College London. He holds a PhD in Signal Processing from Universidad Carlos III de Madrid (UC3M) and completed postdoctoral work at Warwick CS and The Alan Turing Institute. His research bridges computational statistics, machine learning, and applied probability, focusing on stochastic sampling methods, generative modeling, and optimization techniques for statistical inference and inverse problems. Education : BSc/MSc in Electronics and Communications Engineering (İTÜ), PhD in Signal Processing (UC3M). His work explores diffusion-based parameter estimation, score-energy-latent generative models, and overdamped/underdamped Langevin dynamics for probabilistic solutions to PDEs. He has developed adaptive importance samplers and contributed to high-dimensional stochastic filtering with real-world data. Recent efforts include lecture notes and code for stochastic simulation courses and organizing workshops on Gradient Flows for Sampling (Royal Statistical Society, 2023) and Accelerating Generative Models (Alan Turing Institute, 2022-2023). His publications emphasize error bounds in diffusion models, physics-informed neural processes, and convergence analysis of optimization-driven sampling algorithms.

Jennyمشاهده پروفایل

استاد

• Turbulent Combustion Modeling
• Computational Fluid Dynamics
• Rarefied Gas Kinetics
• +۴ مورد دیگر

Professor Jenny leads the Jenny Research Group at ETH Zürich, specializing in turbulent reactive flows, rarefied gas kinetics, and biomedical fluid dynamics. Her work bridges fundamental research with industrial applications in energy systems and fluid mechanics. Develops advanced turbulence models (TDDM, hybrid LES/RANS) for multi-scale flows Pioneers data assimilation frameworks for RANS simulations using adjoint methods Advances particle-based stochastic algorithms for fractured porous media transport Her recent publications emphasize adaptive time integration techniques, probabilistic modeling of non-linear transport phenomena, and optimized simulation tools for hydrogen storage systems. The group's methodological innovations focus on reducing computational costs while maintaining physical accuracy through novel regularization strategies. Key applications include combustion device optimization, high-pressure tank filling analysis, and fractured reservoir simulations. Current projects integrate machine learning with traditional CFD methods to address challenges in droplet clustering, flame surface density propagation, and supersonic spray dynamics. The research framework spans from direct numerical simulations of fundamental flow physics to industrial-scale hybrid modeling implementations.

Ivan Gentilمشاهده پروفایل

مدرس

• Analysis
• Probability Theory
• Geometry
• +۱۰ مورد دیگر

Ivan Gentil is a Lecturer and researcher at the Camille Jordan Institute, affiliated with Claude Bernard University Lyon 1 (member of the University of Lyon). His primary research interests lie in the interplay of Analysis, Probability Theory, and Geometry , with a focus on functional inequalities (logarithmic Sobolev, Sobolev, Poincaré), optimal transport, Hamilton-Jacobi equations, and the asymptotic behavior of diffusion processes. Education: PhD in Mathematics (2001), directed by Michel Ledoux at Paul Sabatier University. Habilitation à Diriger des Recherches (2008), advised by Jean Dolbeault at Paris-Dauphine University. Research and Publications: He has authored over 30 publications, including books on logarithmic Sobolev inequalities and Markov diffusion operators. Recent work explores dimensional contractions in Wasserstein distance, mean field Schrödinger problems, and sharp Beckner-type inequalities. His research often bridges entropy-energy duality, geometric curvature, and probabilistic methods. Community Engagement: Gentil co-organized the Climat et Transitions teaching unit (2021) and a manifesto (2023–2025) to reduce mathematicians' air travel, amassing 650+ signatures. He delivered seminars on mathematical research and climate urgency, including talks at Rennes (2024) and Paris Cité (2025). Students and Collaborations: He advised PhD students Luigia Ripani, Simon Zugmeyer, Gauthier Clerc, and Mohamed Benghazi. He participated in thesis juries for over 20 candidates, collaborating with researchers like Cédric Villani and Christian Léonard.