Tootfinder

Predictive Free Energy Simulations Through Hierarchical Distillation of Quantum Hamiltonians
Chenghan Li, Garnet Kin-Lic Chan
https://arxiv.org/abs/2509.10967 https://

Predictive Free Energy Simulations Through Hierarchical Distillation of Quantum Hamiltonians
Obtaining the free energies of condensed phase chemical reactions remains computationally prohibitive for high-level quantum mechanical methods. We introduce a hierarchical machine learning framework that bridges this gap by distilling knowledge from a small number of high-fidelity quantum calculations into increasingly coarse-grained, machine-learned quantum Hamiltonians. By retaining explicit electronic degrees of freedom, our approach further enables a faithful embedding of quantum and class…

Performance Evaluation of a Fan-Array Wind Tunnel for Advanced Aerodynamic Studies
Kalyani Panigrahi, Rohan Bhattacharya, Sabareesh G. R., Pardha S Gurugubelli
https://arxiv.org/abs/2508.21130

Performance Evaluation of a Fan-Array Wind Tunnel for Advanced Aerodynamic Studies
Wind tunnel's consisting an array of computer controlled/programmable fans have evolved over the last three decades for simulating the atmospheric turbulence to study the aerodynamic loads over bluff bodies such as buildings and bridges. In addition to civil engineering structures, these wind tunnels can also have applications extending towards free-flight aerodynamic tests on drones and unmanned aerial vehicles (UAVs). Achieving velocity profiles such as linearly sheared flows or boundary laye…