A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study uses neural networks to predict wall quantities in turbulent flows.
Neural network predicts turbulence from wall shear stress.
Study rigidity in Riemannian manifolds using Pohozoaev and P-function approaches.
Convolutional networks predict turbulence from wall quantities.
Geometric Algebra Transformer (GATr) handles various geometric data types efficiently.
We present hidden fluid mechanics (HFM), a physics informed deep learning framework capable of encoding an important class of physical laws governing fluid motions, namely the Navier-Stokes equations. In particular, we seek to leverage the underlying conservation laws (i.e., for mass, momentum, and energy) to infer hid…
Paper solves Serrin problem for ring-shaped domains, showing velocity has finitely many maxima.
HFNO enhances interpretability of turbulent flows through parallel wavenumber bin processing.
Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning. Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networks and return detail…