9 citations · 16 across the 20 of their papers we have counts for
22 papers
Discovery of Singularities in Partial Differential Equations Using Adaptive Numerical Discretizations
Allen Yang, Lucas Amoudruz, Petros Koumoutsakos
We compute self-similar singularity profiles of partial differential equations (PDEs) using adaptive numerical discretizations based on optimizing a discrete loss (ODIL). The field…
Data-driven impeller model for efficient large eddy simulations of metastable von Kármán flows
Quentin Malé, Lucas Amoudruz, Daniel Bulgarini +5
The von Kármán turbulent swirling flow exhibits intriguing large-scale metastable dynamics, including low-frequency state switching. The study of state switching demands long-durat…
A hybrid proper orthogonal decomposition and diffusion framework for reduced-order forecasting of turbulent flow dynamics
Rodrigo Abadia-Heredia, Xiangrui Zou, Manuel Lopez-Martin +2
Forecasting turbulent flow dynamics requires a balance between predictive fidelity and computational efficiency. Diffusion-based generative models can represent complex spatiotempo…
A geometry-aligned multi-fidelity framework for uncertainty quantification of wildfire spread
Konstantinos Vogiatzoglou, Costas Papadimitriou, Vasilis Bontozoglou +2
Forward propagation of input uncertainties in physics-based wildfire models is computationally prohibitive, limiting the use of high-fidelity simulators in risk assessment workflow…
Prediction of Extreme Events in Multiscale Simulations of Geophysical Turbulence using Reinforcement Learning
Yifei Guan, Lucas Amoudruz, Sergey Litvinov +4
Accurate subgrid-scale closures are essential for weather/climate models, where predicting extreme events is critical. Traditional closures have structural errors, e.g., producing…
A Critical Assessment of Pattern Comparisons Between POD and Autoencoders in Intraventricular Flows
Eneko Lazpita, Andrés Bell-Navas, Jesús Garicano-Mena +2
Understanding intraventricular hemodynamics requires compact and physically interpretable representations of the underlying flow structures, as characteristic flow patterns are clo…
Generative Urban Flow Modeling: From Geometry to Airflow with Graph Diffusion
Francisco Giral, Álvaro Manzano, Ignacio Gómez +2
Urban wind flow modeling and simulation play an important role in air quality assessment and sustainable city planning. A key challenge for modeling and simulation is handling the…
Scalable, Cloud-Based Simulations of Blood Flow and Targeted Drug Delivery in Retinal Capillaries
Lucas Amoudruz, Sergey Litvinov, Riccardo Murri +4
We investigate the capabilities of cloud computing for large-scale,tightly-coupled simulations of biological fluids in complex geometries, traditionally performed in supercomputing…
Structure tensor Reynolds-averaged Navier-Stokes turbulence models with equivariant neural networks
Aaron Miller, Sahil Kommalapati, Robert Moser +1
Accurate and generalizable Reynolds-averaged Navier-Stokes (RANS) models for turbulent flows rely on effective closures, but currently available closures are notoriously unreliable…
Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss
Lucas Amoudruz, Sergey Litvinov, Costas Papadimitriou +1
Inverse problems are crucial for many applications in science, engineering and medicine that involve data assimilation, design, and imaging. Their solution infers the parameters or…
Ising energy model for the stochastic prediction of tumor islets
Lucas Amoudruz, Gregory Buti, Luciano Rivetti +6
A major challenge in diagnosing and treating cancer is the infiltrative growth of tumors into surrounding tissues. This microscopic spread of the disease is invisible on most diagn…
Data-Driven Discovery of Interpretable Kalman Filter Variants through Large Language Models and Genetic Programming
Vasileios Saketos, Sebastian Kaltenbach, Sergey Litvinov +1
Algorithmic discovery has traditionally relied on human ingenuity and extensive experimentation. Here we investigate whether a prominent scientific computing algorithm, the Kalman…
Controlling Topological Defects in Polar Fluids via Reinforcement Learning
Abhinav Singh, Petros Koumoutsakos
Topological defects in active polar fluids exhibit complex dynamics driven by internally generated stresses, reflecting the deep interplay between topology, flow, and non-equilibri…
Contactless Precision Steering of Particles in a Fluid inside a Cube with Rotating Walls
Lucas Amoudruz, Petr Karnakov, Petros Koumoutsakos
Contactless manipulation of small objects is essential for biomedical and chemical applications, such as cell analysis, assisted fertilisation, and precision chemistry. Established…
Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss
Petr Karnakov, Lucas Amoudruz, Petros Koumoutsakos
Optimal path planning and control of microscopic devices navigating in fluid environments is essential for applications ranging from targeted drug delivery to environmental monitor…
Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data
Lothar Heimbach, Sebastian Kaltenbach, Petr Karnakov +2
Partial Differential Equations (PDEs) describe phenomena ranging from turbulence and epidemics to quantum mechanics and financial markets. Despite recent advances in computational…
Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning
Paul Fischer, Sebastian Kaltenbach, Sergey Litvinov +2
The Lattice Boltzmann method (LBM) offers a powerful and versatile approach to simulating diverse hydrodynamic phenomena, spanning microfluidics to aerodynamics. The vast range of…
Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
Michal Balcerak, Tamaz Amiranashvili, Antonio Terpin +5
Current state-of-the-art generative models map noise to data distributions by matching flows or scores. A key limitation of these models is their inability to readily integrate ava…
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows
Han Gao, Sebastian Kaltenbach, Petros Koumoutsakos
Modeling and simulation of complex fluid flows with dynamics that span multiple spatio-temporal scales is a fundamental challenge in many scientific and engineering domains. Full-s…
Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization
Michal Balcerak, Tamaz Amiranashvili, Andreas Wagner +7
Physical models in the form of partial differential equations serve as important priors for many under-constrained problems. One such application is tumor treatment planning, which…
Data-driven shape inference in three-dimensional steady state supersonic flows using ODIL and JAX-Fluids
Aaron B. Buhendwa, Deniz A. Bezgin, Petr Karnakov +2
We present a novel data- and first-principles-driven method for inferring the shape of a solid obstacle and its flow field in three-dimensional steady-state supersonic flows. The m…
Inertial Focusing of Spherical Particles: The Effects of Rotational Motion
Dmitry Alexeev, Sergey Litvinov, Athena Economides +3
The identification of cells and particles based on their transport properties in microfluidic devices is crucial for numerous applications in biology and medicine. Neutrally buoyan…