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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 ↗299 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
Anthony James Leggett: Explorer of quantum mechanics, from fluids and solids to the macroscopic world ↗
Tony Leggett (1938-2026) was one of the great theoretical physicists of his generation, specializing in the quantum mechanics of fluids and solids. Rightly recognized by the 2003 Nobel Prize in Physics for his explanation of the experiments discovering the superfluidity of helium-3, he will be remembered equally for his pioneering inquiries into the transition from the quantum to the classical world and the extent to which we can observe quantum phenomena on macroscopic scales.
- OpenAlex · Published · First archived
Physics-Guided Dual-Branch Domain Generalization with Confidence-Adaptive Decision Fusion for Cross-Speed Fault Diagnosis of Planetary Gearboxes ↗
Data distribution shift across different rotational speeds limits domain generalized fault diagnosis for planetary gearboxes. Existing single-representation domain generalization (DG) methods degrade severely in cross-speed extrapolation, with different representations showing speed-dependent advantages. The core contribution of this paper is a sample-wise confidence-adaptive decision fusion mechanism under the pure DG constraint. For each test sample, the fusion weights are computed online from the relative softmax prediction confidence of the two branches. The mechanism requires neither targ…
- OpenAlex · Published · First archived
Multi-graph interactive oil production prediction network integrating physics priors and data-driven approaches ↗
Abstract In the digital transformation of smart oilfields, accurate oil production prediction is essential for optimizing production strategies. However, the production process is a complex nonlinear system involving coupled electrical, fluid, and thermodynamic domains where sensor data is high-dimensional and noisy. Traditional mechanistic models struggle with parameter calibration, while standard data-driven models like LSTM often overlook spatial topological relationships and physical constraints. To address these challenges, this paper proposes the multi-graph interaction learning network…
- OpenAlex · Published · First archived
Mission-Profile-Aware Physics-of-Failure Reliability Assessment and Quantitative FMEA of a 10 kW Solid-State Transformer ↗
Solid-state transformers (SSTs) are pivotal for next-generation power systems, yet their widespread adoption is hindered by reliability uncertainties under diverse operational conditions. This paper introduces a mission-profile-aware, physics-of-failure (PoF) reliability assessment framework that explicitly links operating conditions, degradation mechanisms, and statistical uncertainty. The approach combines PoF models for stress-sensitive components with handbook-based reliability models for passive elements, employing Joint Electron Device Engineering Council (JEDEC)-aligned statistical meth…
- OpenAlex · Published · First archived
Mechanics of electroadhesion by polycation interfacial bridging: sticky electrophoresis, ionic complexation and chain entanglement ↗
Abstract An e-GLUE is a polymer network containing interpenetrating polycations, which can bond the anionic network of mucosa by polycation interfacial bridging under an electric field. This paper formulates a theoretical model to study such electroadhesion by electrophoresis of polycations, ionic complexation between polycations and the anionic network, and polycation-network entanglement. We use a diffusion- drift model coupled with a Bell-like field-dependent chain friction to describe the sticky electrophoresis of polycations in the anionic network. We determine the formation of ionic bond…
- OpenAlex · Published · First archived
Quantum correlations of tripartite mixed states in the black hole quantum atmosphere ↗
Abstract We investigate quantum state texture, genuine multipartite entanglement, and tripartite nonlocality of a tripartite mixed state in the black hole quantum atmosphere. By introducing the Hartle-Hawking local temperature into the Bogoliubov coefficients, we characterize the influence of the local Hawking effect on both physically accessible and inaccessible reduced states. We find that the extrema of these three quantities all lie in the same near horizon region and shift outward with increasing local Hawking temperature, coinciding with the peak region of the local Hawking temperature a…
- OpenAlex · Published · First archived
Error Bounds for Physics Informed Neural Networks in Generalized KdV Equations Placed on Unbounded Domains ↗
In this paper we study a rigorous setting for the numerical approximation via deep neural networks of the generalized Korteweg-de Vries (gKdV) model in one dimension, for subcritical and critical nonlinearities, and assuming that the domain is the unbounded real line. The fact that the model is posed on the real line makes the problem difficult from the point of view of learning techniques, since the setting required to model gKdV is structured on intricate oscillatory estimates dating from Kato, Bourgain and Kenig, Ponce and Vega, among others. Therefore, a first task is to adapt the setting…
- OpenAlex · Published · First archived
Quantum-inspired machine learning: a survey ↗
Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks. However, current review literature often presents a superficial exploration of QiML, focusing instead on the broader Quantum Machine Learning (QML) field. In response to this gap, this survey provides an integrated and comprehensive examination of QiML, exploring QiML's diverse research domains including tensor network simulations, dequantized algorithms, and others, showcasing recent…
- OpenAlex · Published · First archived
A Physics-Data Dual-Channel Collaborative Cross-Domain Transfer Learning Fault Diagnosis Method for Main Circulation Pump in Converter Valve Cooling Systems ↗
To address the three major challenges in fault diagnosis of main circulating pumps in converter station valve cooling systems—small-sample scenarios, cross-domain distribution shift, and lack of physical interpretability—a physics-data dual-channel collaborative cross-domain transfer learning framework is proposed. The physics channel designs a physics-constrained conditional generative adversarial network (PC-cGAN), which explicitly embeds the pump's Euler equation into the loss function to constrain the hydraulic characteristics of generated samples. The data channel constructs a multi-sourc…
- OpenAlex · Published · First archived
Chemistry-transferable physics-informed neural network surrogate for lithium-ion cells with online continual learning ↗
This paper presents a physics-informed neural network (PINN) surrogate of the single particle model (SPM) for predicting lithium-ion concentrations and terminal voltage under varying operating and aging conditions. The proposed approach employs a two-stage learning framework. In the offline stage, the network learns the nominal electrochemical dynamics by enforcing the governing partial differential equations along with their associated initial and boundary conditions. During online operation, the physics-informed hidden layers are held fixed, and only the parameters of the output layer are ad…
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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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