On-device research index

arXiv research

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.

168,657 papers · 148 categories

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4 results for “Physics-ML”

Hybrid Physics-ML Model for Forward Osmosis Flux with Complete Uncertainty Quantificationcs.LG

A hybrid physics-ML model predicts FO water flux with high accuracy and uncertainty quantification.

problem Challenges in accurately modeling Forward Osmosis water flux due to complex internal mass transfer phenomena.

method Robust Hybrid Physics-ML framework using Gaussian Process Regression (GPR) for uncertainty-aware Jw prediction.

result Achieved a state-of-the-art MAPE of 0.26% and R2 of 0.999 on independent test data.

Forward Osmosis Physics-ML Gaussian Process Regression Uncertainty Quantification Water Flux Prediction

2025-12-11abs ↗pdf ↗

Hybrid Physics-ML Framework for Pan-Arctic Permafrost Infrastructure Risk at Record 2.9-Million Observation Scalestat.ML

Hybrid framework predicts Arctic permafrost decline, risks infrastructure, and provides tools.

problem Tackles permafrost decline and infrastructure risk assessment in Arctic territories.

method Hybrid physics-machine learning framework integrating 2.9 million observations.

result Projects mean permafrost fraction decline of -20.3 pp under RCP8.5 forcing, with high-risk zones identified.

Arctic permafrost risk assessment machine learning climate change infrastructure

2025-10-02abs ↗pdf ↗

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systemsphysics.comp-ph

Paper integrates ML with physics models for engineering and environmental challenges.

problem Complex science and engineering problems require new methodologies combining physics-based models and ML.

method Structured overview of integrating physics-based models with ML techniques.

result Taxonomy of existing techniques and potential research gaps identified.

Machine Learning Physics-based Modeling Engineering Environmental Systems Hybrid Models

2020-03-10abs ↗pdf ↗

Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigmq-bio.BM

Physics-informed machine learning models improve biomolecular system simulations.

problem Modeling unresolved interactions beyond classical force fields.

method Physics-informed neural networks and operator learning.

result Accurate, mechanistic, generalizable models for long-timescale kinetics.

machine learning biomolecular systems physics-informed neural networks operator learning

2025-11-10abs ↗pdf ↗