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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.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
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
Operator-Budget Evaluation of Residual-Adaptive Sampling in Physics-Informed Neural Networks ↗
Residual-adaptive collocation is commonly compared at equal optimizer steps, although maintaining its sampling proposal requires PDE-residual evaluations in addition to those used for training. Such comparisons can obscure whether an apparent gain comes from point placement or from additional information work. We introduce an operator-budget evaluation framework that records training and proposal-maintenance residual calls in a common ledger, distinguishes fixed-update cost from work-matched accuracy, and requires a joint cost–accuracy decision. Amortized residual proposal maintenance (ARPM) s…
- OpenAlex · Published · First archived
A Macro-Anchored Physics-Informed GAN for RUL Prediction Under Continuous Block Missing Data ↗
Continuous block missingness in condition monitoring data poses a major challenge to the reliability of Remaining Useful Life (RUL) prediction for industrial equipment. Existing frameworks, including direct prediction from incomplete observations and decoupled two-stage imputation–prediction pipelines, suffer from a noticeable performance decline under prolonged data voids. Although Generative Adversarial Network (GAN)-based generative models have shown promise in data repair, they remain limited by dimensionality contamination from fixed-dimension architectures, purely data-driven over-smooth…
- OpenAlex · Published · First archived
Graded identification of air leakage in coal-mine gas drainage boreholes using a physics-augmented transformer–LSTM framework ↗
Air leakage in gas drainage boreholes in coal mines reduces methane concentration and extraction efficiency, yet reliable grading remains difficult because weak leakage signatures can be obscured by noise and changes in operating conditions. This study proposes a physics-augmented Global–Local Transformer–LSTM (GL-TransLSTM) framework for classifying normal, slight, moderate, and severe leakage. The four grades were defined in controlled underground tests by setting the valve opening of a drainage branch exposed to ambient mine air to 0%, 10%, 50%, and 100%, respectively. Complete ensemble emp…
- OpenAlex · Published · First archived
Quantum neural network-based modeling of radiative magneto-ternary nanofluid flow over a porous stretching surface with enhanced thermal transport analysis ↗
The research studied how nonlinear fluid mechanics affects motion and heat transfer combined phase mix on porous surfaces, with A l 2 O 3 − C u − T i O 2 / H 2 O nanofluids moving at various rates to simulate the presence of a stretched porous surface. It will be important to include nonlinear radiation, viscous dissipation, and suction/injection effects in this investigation. To model transport processes in the examined material systems a novel framework based on quantum intelligent models (inspired by quantum computing and machine learning using Bayesian regularization) was developed and…
- OpenAlex · Published · First archived
Research and Experimental Evaluation of an Electric-Drive Maize Precision Seeding System Integrating Physics-Informed Neural Networks and PID Control ↗
To address the problem of decreased rotational speed tracking accuracy of the seeding motor caused by system nonlinearity, time-varying parameters, and field disturbances during high-speed electric-driven precision seeding, a PINN-PID control method was proposed. First, the kinematic relationship among operating speed, target plant spacing, and seeding motor rotational speed was established, and a motor dynamics model was constructed. Subsequently, the data supervision error, seeding kinematic residual, motor dynamics residual, and smoothness constraint were jointly embedded into the neural ne…
- OpenAlex · Published · First archived
Physics informed muon scattering tomography with momentum integration for nonintrusive monitoring of microreactor cores ↗
Abstract Next-generation microreactors enable remote deployment and semi-autonomous operation, but compact, sealed, heterogeneous cores limit conventional safeguard approaches that rely on access and bulk accountancy. Limited inspection access and complex internal geometry reduce sensitivity to localized anomalies such as missing fuel. Here we demonstrate missing-fuel detection in microreactor scale geometries using muon scattering tomography under realistic cosmic-ray conditions. We introduce $$\mu$$ TRec, a physics-informed framework that reconstructs event-level curved muon trajectories by…
- OpenAlex · Published · First archived
Physics-guided attention neural network for interpretable and robust photovoltaic fault classification with strict external validation ↗
Accurate and interpretable fault diagnosis in photovoltaic (PV) systems is essential for improving operational reliability and reducing energy losses. This study proposes a Physics-Guided Attention Neural Network (PGANN) for three-class PV fault classification, including Open-circuit, Short-circuit, and Partial Shading conditions. The proposed framework integrates physics-guided feature engineering into the input representation through calculated power, power error, and normalized voltage ratio, thereby embedding electrical-domain knowledge without imposing unstable physics-based penalty terms…
- OpenAlex · Published · First archived
Scalable ground-state certification of quantum spin systems via structured noncommutative polynomial optimization ↗
A fundamental challenge in quantum physics is determining the ground-state properties of many-body systems. Whereas standard variational approaches posit a wave-function ansatz and minimize over the possible states expressible by that ansatz, the problem can alternatively be formulated as a noncommutative polynomial optimization problem and treated through a hierarchy of semidefinite programming relaxations. In contrast to variational calculations, these relaxations provide lower bounds on ground-state energies and both lower and upper bounds on observable expectation values. However, this app…
- OpenAlex · Published · First archived
On the power of multipartite entanglement for pseudotelepathy ↗
As early as 1935, Schrödinger recognized entanglement as “not one but rather the characteristic trait of quantum mechanics, the one that enforces its entire departure from classical lines of thought.” Indeed, most remarkable phenomena in quantum information science, such as quantum computing and quantum teleportation, spring from clever uses of entanglement. Among them, pseudotelepathy enables two or more players to win systematically at some cooperative games with no need for communication between them, a restriction that would make the task impossible in a classical world. We investigate the…
- OpenAlex · Published · First archived
An optimized construction of lie algebra generator pools for variational quantum eigensolvers in chemistry ↗
Abstract Lie algebras are essential mathematical structures used in physics to describe sets of quantum operators. Identifying a minimal set of generators to construct these algebras is a central challenge. The traditional search for such generators relies on greedy construction steps applied to an exponentially growing number of candidate operators, making it computationally intractable. Here we show a general, polynomial-scaling strategy, based on fundamental Lie-algebraic properties, to overcome this bottleneck. We apply this framework to quantum chemistry, specifically to adaptive variatio…
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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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