Latest Research in Model Reduction and Neural Networks

Top Research Topics in Model Reduction and Neural Networks

• Model Reduction and Neural Networks — 3 papers
• Fluid Dynamics — 2 papers
• Thermoelastic and Magnetoelastic Phenomena — 1 papers
• Erosion and Abrasive Machining — 1 papers
• Computational Fluid Dynamics and Aerodynamics — 1 papers
• Fluid dynamics and aerodynamics studies — 1 papers
• Analysis of PDEs — 1 papers
• Machine Learning — 1 papers
• Atmospheric and Oceanic Physics — 1 papers

Highest-Cited Papers

1. Viscous Implicit Overset Method for High-Speed Compressible Flows (1 citations)
2. A stabilized finite element method for a Morpho-Visco-Poroelastic Model
3. IB-Flows: an open-source multi-GPU immersed boundary code for fluid-structure interaction
4. Physics-guided deep learning for real-time resonance diagnostics and predictive maintenance in pulsed water jet manufacturing systems
5. Physical Instability and Dynamical Constraints of Axisymmetric Contracting Vortex Singularity Constructions
6. Exact Formulation of Non-equilibrium Energy Cascades in High-Reynolds Turbulence via Rough Operator Algebra (ROA) and Seonggil Matrix Theory
7. Physics-Informed Machine Learning in Subsurface Multiphysics Flow Modeling: Integrating Physical Constraints for Accelerated Simulation
8. Existence of thermodynamically consistent solutions for data-driven porous media problems
9. Disentangling Attention in Deep Operator Learning: A Controlled Study of Data-Driven and Physics-Informed Architectures
10. Growth of small localized perturbations in Surface Quasi-Geostrophic turbulence
11. Towards Rapid Prototyping of Spray Injectors: A Regime-Agnostic Neural Operator Surrogate for Gas-Liquid Interface Evolution
12. A Universal Minimax Lower Bound for Causal Lagrangian TrackingFrom empirical flow-family classification to an algorithm-independent impossibility certificate with object-shape-aware tolerance