AI & ML
Hamiltonian Interface Dynamics for Reduced-Order Optimization of Incompressible Mixing
arXiv cs.NA0 views
arXiv:2605.04688v2 Announce Type: replace Abstract: We develop a reduced-order framework for optimizing mixing in two-dimensional incompressible flows. Instead of optimizing the full transport PDE, the method maximizes the length of advected material interfaces, leading to a Hamiltonian control problem with a finite spatial basis based on parametrized stream functions. We derive the continuous adjoint equations and reduced gradients, and discretize the forward and adjoint dynamics with the implicit midpoint rule. The resulting discrete adjoint is algebraically consistent with the derivative of the fully discrete objective when the implicit equations are solved exactly. The approach applies to bounded two-dimensional domains with sufficiently regular finite-dimensional stream-function parametrizations. Numerical experiments on cellular-flow and Doswell frontogenesis benchmarks show interface stretching and mix-norm decay over the simulated horizons. In four comparisons, the interface runs have lower recorded optimization times, whereas the Sobolev controls attain smaller terminal mix-norms. The different constraints, stopping rules and timing scopes preclude an equal-accuracy speedup claim.
Source:https://arxiv.org/abs/2605.04688PreviousImportance sampling and active subspace in quasi-Monte CarloNextNo-Harm Physics-Informed Inverse Learning with Residual-Calibrated Uncertainty
Related posts

GPT-Image-2.6 Outputs SVG and GIFs. Unlike 2.5, You Can't Run It Through Batch
0dev.to

Designing a Form That Asks for Birth Date, Time and Place (No Code, Just the Tricky Parts)
0dev.to UX
메타 Quest 2 OS 업데이트 종료, 약시 훈련 루틴에는 무슨 일이 생기나
0dev.to News
How to Put a Logo in a QR Code (and Make Sure It Still Scans)
0dev.to Design
Why Luxury Fine Jewelry Brands Are Ditching Physical Video Shoots for 3D CAD Commercials in 2026
0dev.to Design

How AI Is Changing Healthcare in 2026 - And Why Developers Should Pay Attention
0dev.to Productivity