
Explore extraordinary waves, from historical solitary waves to modern solitons, and their applications in water, rogue waves, photonics, and cellular automata.

Explore computational modeling techniques for active systems, focusing on advanced concepts and practical applications in the field of active matter.

Explore groundbreaking research on galaxy formation through multi-scale simulations, examining how small-scale atomic processes and large-scale cosmic phenomena shape galactic evolution and structure formation.

Delve into advanced mathematical analysis of fluid interfaces and vortex filaments, exploring gravity Stokes interfaces, Muskat problem, and vortex behavior in Navier-Stokes equations.

Explore advanced optoelectronic systems for neural interfaces, focusing on miniaturized electronics and their applications in deep brain stimulation, monitoring, and neuroscience research.

Explore simultaneous measurements of concentration and velocity fields at shock-accelerated interfaces in high-energy-density science experiments.

Explore laser-driven tin ejecta microjet interactions through experimental observations in high energy density science.

Explore automatic differentiation's scientific applications beyond machine learning. Discover innovative approaches in PDEs, self-assembly, fluid dynamics, and protein modeling using large-scale optimization.

Explore Covid-19 transmission modeling across scales, from high-resolution simulations of respiratory aerosols to broader exposure assessment tools, aiding in pandemic mitigation strategies.

Explore high-resolution digital twins of vascular anatomy to inform disease treatment. Learn how data science and physics-based modeling combine to address personalized medicine challenges.

Explore autoencoders for aerodynamic predictions, focusing on flow past wing geometries. Learn about non-linear compression, latent representations, and uncertainty quantification in data-driven modeling.

Explore innovative neural network-based techniques for efficient reduced-order modeling of complex parametric partial differential equations, focusing on intrusive and interpretable methodologies.

Explore machine learning strategies for neural PDE solvers, focusing on self-supervised learning and simulation-in-the-loop approaches to enhance spatiotemporal modeling across various systems.

Explore Neural Differentiable Physics, unifying numerical PDEs and deep learning for enhanced computational modeling of complex physical systems. Learn innovative SciML approaches integrating physics principles with AI.

Explore Koopman Operator Theory for machine learning of dynamical systems, covering applications in fluid dynamics, power grids, network security, and more.

Explorez les singularités en temps fini dans les fluides, en examinant les résultats récents et classiques, les preuves assistées par ordinateur et les applications de l'apprentissage automatique.

Explore smooth steady transonic flows in compressible Euler equations, focusing on mixed types, degeneracy, and challenges in proving existence for flows with rotations and non-zero vorticities.

Explore wavelet-inspired Nash's iteration in fluid dynamics, focusing on advanced mathematical techniques for analyzing Euler and Navier-Stokes equations.

Automate Computational Fluid Dynamics processes using AirShaper's innovative techniques. Enhance efficiency and accuracy in fluid flow simulations.

Explore incompressible fluid theory, from Leray's weak solutions to Ladyzhenskaya's contributions, and recent findings on Navier-Stokes equations and turbulence.

Explore error estimates in anomalous dissipation and the Strong Onsager Conjecture, delving into mathematical descriptions of turbulent flows and energy transfer between scales.