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Earlier sources: Physics
This science thread follows physics through original research, public projects, and source reports. Read the evidence below to see which developments are represented in SYMTRI’s archive.
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Open in the map ↗309 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
A Physics-Informed Temporal Convolutional Network for Joint Fault Diagnosis and Calibrated Severity Estimation in Induction Motors Through Motor Current Signature Analysis ↗
Motor current signature analysis (MCSA) enables non-invasive condition monitoring of three-phase induction motors, but most published studies use short laboratory records with artificially seeded faults and report only a discrete fault label without any measure of confidence. This paper proposes a physics-informed temporal convolutional network (PI-TCN) that is trained and tested on 12 months of stator-current records measured on a 90 kW industrial induction motor in service. Fault labels were assigned retrospectively by aligning the recordings with documented maintenance interventions. From e…
- OpenAlex · Published · First archived
Physics-Informed Neural Network Surrogate Modeling of Three-Dimensional Gas–Liquid Hydrodynamics in Aerated Double-Impeller Stirred Tanks ↗
This study develops a physics-informed surrogate for predicting three-dimensional gas–liquid hydrodynamics in an aerated double-impeller stirred tank. Reference fields were generated using steady-state computational fluid dynamics (CFD) simulations in ANSYS Fluent at impeller speeds of 200–500 rpm. Three models with identical network architectures were examined: a supervised neural network (NN) trained on CFD data, a boundary-informed neural network (BINN) incorporating boundary constraints, and a physics-informed neural network (PINN) incorporating boundary and reduced physics constraints. At…
- OpenAlex · Published · First archived
Data-Driven On/Off Topology Optimization of Electromagnetic Devices Based on Graph Physics-Informed Neural Networks and Physics-Guided Reinforcement Learning ↗
Topology optimization of electromagnetic devices is computationally formidable owing to the combinatorial on/off material distribution on unstructured finite element meshes and repeated Maxwell equation solves. This paper develops GPRL-TO, a data-driven framework addressing both difficulties through three synergistic components. First, a graph physics-informed neural network (G-PINN) reformulates the finite element mesh as a graph and predicts element-wise magnetic flux densities under embedded magnetoquasistatic constraints, with two to three orders of magnitude speedup over finite element an…
- OpenAlex · Published · First archived
Physics-Guided Residual Learning for Full-Angular-Domain Scattering Prediction of Coding Metasurfaces ↗
Coding metasurfaces provide an effective approach for manipulating electromagnetic scattering through discrete coding states. However, obtaining angular-domain scattering responses for a large number of coding configurations remains computationally expensive with full-wave electromagnetic simulations. To address this issue, this work proposes a Physics-Guided Residual Learning (PGRL) framework for rapid radar cross section (RCS) prediction over the full angular domain. A discrete-aperture model provides an approximate physics-based scattering prior, and a residual convolutional neural network…
- OpenAlex · Published · First archived
Physics-guided residual regression and empirical-error-constrained design exploration for frp-reinforced concrete slab–column connections ↗
Abstract Punching shear is a brittle limit state in concrete flat slabs, and its assessment is still uncertain when fiber-reinforced polymer (FRP) bars replace steel flexural reinforcement. This study develops a physics-guided residual-ridge model for interior, concentrically loaded FRP-reinforced slab–column connections without shear reinforcement. The development database includes 143 tests from 28 experimental programs: 93 GFRP, 39 CFRP, and 11 BFRP specimens. A modified KDS 14 formulation provides the mechanical baseline, and ridge regression estimates the logarithmic test-to-model residua…
- OpenAlex · Published · First archived
Physics-constrained liquid neural network for wind-induced vibrations prediction of hanger cables on long-span suspension bridges ↗
Wind-induced vibrations (WIVs) of hanger cables on long-span suspension bridges could accelerate fatigue in suspension bridge suspension cables, damage damping devices, and other serviceability issues. The existing high-fidelity fluid-structure simulations are costly for real-time capable monitoring and purely data-driven approaches often violate basic physical laws and struggle to generalize under changing wind conditions. Prediction of hanger cable WIVs under strong non-stationarity and cross-domain distribution shifts has long faced challenges such as insufficient generalization, extrapolat…
- OpenAlex · Published · First archived
Neural network-based multipartite entanglement classification with prior guidance from quantum uncertainty relations ↗
Quantum entanglement is a crucial resource in quantum information processing, yet its efficient, scalable and robust classification in multipartite systems remains theoretically challenging. Although supervised machine-learning has been applied to this task, most existing methods still suffer from high measurement costs, computational consumption, and weak noise robustness. In this work, by incorporating multipartite uncertainty relations as prior guidance, we propose a neural network approach to classify distinct Stochastic Local Operations and Classical Communication (SLOCC) multipartite ent…
- OpenAlex · Published · First archived
The Super Bigbite Spectrometer Physics Program ↗
The structure of the nucleon is a central problem in strong interaction physics in the non-perturbative regime. Indeed, the vast majority of the known matter in the Universe is made of protons and neutrons which are a remarkable emergent phenomenon of quantum chromodynamics. A critical aspect of investigating nucleon structure experimentally is the measurement of fundamental quantities such as the elastic nucleon form factors. Also important is the measurement of the transverse momentum dependent distribution functions. Accessing such quantities experimentally, however, is challenging because…
- OpenAlex · Published · First archived
States of 2D Yang-Mills and large-volume entanglement ↗
A bstract We explore the relation between geometry, topology and entanglement for quantum states in two-dimensional Yang-Mills theory. The most familiar class of states in this theory are states defined by Euclidean path integrals over Riemann surfaces. Bipartite states of this class have thermofield double structure, with entanglement consistently reducing with total area and the number of topological defects, becoming separable in the infinite-area limit. In contrast, Wilson lines and loops generate rich non-monotonic behavior of the entanglement entropy. Most notably, we find that for a cer…
- OpenAlex · Published · First archived
A residual weighted physics informed neural network for forward and inverse problems of reaction diffusion equations ↗
Physics-Informed Neural Networks (PINNs) constitute an effective deep learning approach for solving partial differential equations (PDEs). In this work, we propose a Residual-Weighted Physics-Informed Neural Network (RW-PINN), a new method designed to enhance the accuracy of PINNs -based algorithms. We construct a deep learning framework with two residual-weighting schemes to solve reaction–diffusion equations and evaluate its performance on both forward and inverse problems. The approach computes weights proportional to the PDE residuals, rescales them, and incorporates these scaled residuals…
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How to assess evidence about Physics
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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