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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 ↗303 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
Physics-Informed Photovoltaic Fault Diagnosis Using Hard-Clamped Feature Engineering and Progressive Fine-Tuning ↗
Limited labeled fault data and discrepancies between simulated and measured signals complicate photovoltaic (PV) fault diagnosis. This study develops a transfer learning framework that combines hard-clamped electrical features with progressive fine-tuning. Four measured channels are expanded into a ten-dimensional (10D) representation containing power, an equivalent-resistance indicator, and consecutive-sample differences. A multi-scale convolutional neural network (MSCNN), bidirectional long short-term memory (BiLSTM), and self-attention form the sequence classifier. The model is pretrained o…
- OpenAlex · Published · First archived
Physics-informed explainable machine learning and thermodynamic optimisation of CNT-based nanolubricants for enhanced vapour compression refrigeration system performance ↗
The significance of refrigeration lies in decreasing electricity consumption. However, carbon nanotube (CNT) nanolubricants have nonlinear effects that are challenging to predict through traditional analysis. This research develops a comprehensive experimental, thermodynamic, explainable machine learning (ML), and physics-based optimisation framework for the refrigeration system with R134a working fluid and CNT/polyolester (POE) nanolubricant. Experimental data for different concentrations of CNT ranging from 0 to 1 wt% are analysed without data pre-processing, whereas states, refrigeration ef…
- OpenAlex · Published · First archived
A noise-aware physics-informed neural network method for nonlinear consolidation with non-Darcian flow and continuous drainage boundary ↗
Accurate characterization of soft soil nonlinear consolidation with non-Darcian flow and complex boundary conditions is fundamental to assessing foundation stability. This study presents a Noise-Aware Physics-Informed Neural Network (NA-PINN) framework for one-dimensional nonlinear consolidation problems involving a continuous drainage boundary and non-Darcian flow. Multiple nonlinear couplings can cause gradient imbalance in standard PINNs. Furthermore, when measurement noise and sparse data are present, the inversion of the flow index is susceptible to ill-posedness. To address this problem,…
- GitHub · Repository created · First archived
YLXDXX/gaokao-physics-archive ↗
高考物理真题·LaTeX档案库
- OpenAlex · Published · First archived
Numerical Recovery of Pore-Air Pressure and Effective-Stress Reduction in Cover Soils Using a Two-Phase Inverse Physics-Informed Neural Network ↗
Transient pore-air pressure in gas-loaded cover soils is difficult to observe between monitoring depths. We develop a coupled water–air inverse physics-informed neural network as a numerical proof of concept. Under a pre-calibrated constitutive model, three numerically sampled depths supply pore-air pressure, pore-water pressure, and saturation targets. An independent air-pressure field represents overpressure; the coupled balances constrain the joint state. On a one-dimensional same-equation benchmark, the full-window gas-pressure error is 0.0463. The data-only ablation reaches 0.0043, while…
- OpenAlex · Published · First archived
Parameter Estimation and Physics-Structured Model Compression of Grid-Connected Inverter Systems ↗
The growing deployment of renewable energy systems calls for accurate parameter estimation and compact modeling of grid-connected inverter systems (GCISs). This paper presents a framework for parameter estimation and physics-structured model compression using Physics-Structured-Informed Neural Networks (Ψ-NNs). A teacher physics-informed neural network (PINN) estimates LC-filter parameters by combining measurements with governing equations. A regularized student learns the teacher mapping through physics-informed distillation. Hierarchical clustering identifies shared weight magnitudes, enabli…
- OpenAlex · Published · First archived
Physics informed neural networks with self guided mutual distillation between Kolmogorov Arnold networks and multilayer perceptrons for multidimensional forward and inverse Biot consolidation analysis ↗
Many geotechnical and underground engineering problems involve coupled fluid solid interactions in saturated porous media. Drainage boundaries, material interfaces, nonmonotonic pressure responses, and long time dissipation remain challenging for conventional physics informed neural networks (PINNs). Existing studies on Kolmogorov-Arnold networks (KANs) mainly compare standalone architectures with multilayer perceptrons (MLPs), while their complementary representation characteristics are rarely coordinated for coupled multiphysics problems. This study proposes a novel SGMD-PINN, a physics info…
- OpenAlex · Published · First archived
Physics-informed Kolmogorov–Arnold networks (PIKANs) for noise-robust reconstruction of boiling heat flux from infrared measurements ↗
Recovering a noise-amplified inverse quantity from corrupted boundary data is a recurring problem in physics-informed learning for real-world applications. Specifically, small perturbations in the measured field are magnified through the spatial derivatives needed to reconstruct the target, and hence conventional discretization solvers turn them into large unphysical oscillations. We study this problem through the reconstruction of local time-resolved nucleate-boiling heat flux, which cannot be measured directly and must be inferred from infrared surface temperatures by solving an unsteady thr…
- OpenAlex · Published · First archived
The Everything-is-a-Quantum-Wave Interpretation of Quantum Physics ↗
I outline what I think is the most natural interpretation of quantum mechanics. By natural, I simply mean that it requires the least amount of excess baggage and that it is universal in the sense that it can be consistently applied to all the observed phenomena including the universe as a whole. I call it the Everything-is-a-Quantum-Wave Interpretation because I think this is a more appropriate name than the Many Worlds Interpretation. Here, I explain why this is so. Quanta 2026; 15: 19–23.
- arXiv · Published · First archived
Topology-independent quantum advantage in communication networks ↗
Quantum communication can outperform classical communication in a variety of information-processing tasks, but such advantages are typically established for a fixed communication network. Here we introduce topology-independent quantum advantage, in which a quantum protocol outperforms every classical protocol subject to the same communication constraint, irrespective of how the classical communication resource is distributed among the parties or which communication topology is employed, even when the parties may share unlimited classical randomness. We first demonstrate this phenomenon through…
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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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