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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 ↗317 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.
- Hacker News · Posted on Hacker News · First archived
The Truth Physics Can No Longer Ignore ↗
Hacker News discussion.
- OpenAlex · Published · First archived
A Physics-Informed ConvFormer with State-Space Gating for Smartphone-Based Human Activity Recognition on the WISDM Dataset ↗
Smartphone-based human activity recognition (HAR) requires discriminative temporal modeling and careful assessment of computational cost. This study evaluates a Physics-Informed ConvFormer with State-Space Gating for six-class HAR on the WISDM v1.1 accelerometer dataset. The model combines raw acceleration with deterministic magnitude, directional, gravity-estimate, and dynamic-acceleration features, followed by large-kernel depthwise convolution, a Mamba-inspired causal gate, attention, and a multilayer perceptron. The main subject-level experiment achieved 95.63% best validation accuracy and…
- OpenAlex · Published · First archived
A physics-informed B-spline reconstruction of flow data ↗
Continuous B-spline models provide compact, differentiable representations of discrete flow snapshots, but data-only fitting does not control consistency with prescribed governing equations. We introduce physics-informed B-spline reconstruction (PI-BSR), a post hoc method that optimizes a fixed tensor-product B-spline representation of a completed flow trajectory. PI-BSR combines data fidelity with soft residuals for the governing partial differential equations, initial and boundary conditions, and integral balances over spatial subdomains. A shared-face construction ensures algebraic cancella…
- OpenAlex · Published · First archived
Can physics review catch prescription and PTV margin errors in fully automated radiotherapy plans? A hazard study ↗
Abstract Purpose End‐to‐end AI‐based automated contouring and radiotherapy planning systems promise substantial gains in efficiency and consistency but also introduce new safety risks, including user input errors that occur early in the workflow. This study evaluates the ability of standard physics plan review to detect user errors in prescription and planning target volume (PTV) margin entry in a fully automated planning workflow. Methods A prototype of the Radiation Planning Assistant (RPA) was used to generate fully automated prostate cancer treatment plans for 20 patients. Five cases inten…
- OpenAlex · Published · First archived
Physics-Based Waveform Representation for Geometry-Aware Object Recognition ↗
This paper presents the RaDICAL sensing framework, a monostatic passive radar concept that combines a Sparse Uniform Circular Array (SUCA), deterministic multifrequency dither, and dictionary-based waveform recognition for target detection and classification. Rather than forming conventional spatial images or relying primarily on Doppler processing, RaDICAL encodes target geometry directly into a composite receiver waveform and performs hypothesis testing by matching measured signals to a library of predicted responses. This study develops the SUCA-based signal model for point and extended tar…
- OpenAlex · Published · First archived
Physics-informed machine learning for robust calibration and physiological validation of wearable electrochemical sweat biosensors for metabolite monitoring ↗
Wearable medical devices represent a most adequately investigated research paradigm inherent to their continuous, non-invasive health monitoring capabilities. Electrochemical biosensors are the basic units in such devices, and they are responsible for reliable biomarker measurements from non-invasive biofluids like sweat. However, such biosensors are often affected by calibration drift, which directly influences the reliability of their results. Biosensor responses are affected by numerous factors like device-dependent parameters, environmental variability, and complex physiological factors. T…
- OpenAlex · Published · First archived
Noise-enhanced quantum kernels on analog quantum computers for estimating the non-Markovianity from sparse temporal data ↗
The quantum kernel method, a promising quantum machine learning algorithm, possesses substantial potential for demonstrating a quantum advantage. While most quantum kernels are constructed in the context of gate-based quantum circuits, inspired by the idea of analog quantum computing with Rydberg atoms, here we construct an analog quantum kernel and a hybrid quantum kernel, and demonstrate their competitiveness against other standard baselines in the physically motivated task of estimating the non-Markovianity from sparse temporal data. Crucially, we report a counterintuitive phenomenon where…
- OpenAlex · Published · First archived
Reconstructing Soft X‐ray Photon Count Rates With Missing Segments: A Physics‐Guided Deep Learning Approach ↗
Abstract Soft X‐ray photon count rates are widely used to study interactions between the solar wind and the terrestrial magnetosphere. Soft proton flares frequently contaminate these observations, and screening the contaminated intervals leaves gaps in otherwise continuous time series. We propose a deep learning framework that imputes the missing 0.5–0.7 keV photon count rates from bidirectional temporal context together with simultaneous solar wind and geomagnetic parameters, using the 2.5–5.0 keV band as a background reference. The model employs transformer encoder blocks to leverage bidirec…
- OpenAlex · Published · First archived
Locally adaptive Physics-Informed Neural Networks for nonlinear partial differential equations ↗
Physics-Informed Neural Networks (PINNs) have emerged as a powerful paradigm for solving forward and inverse problems governed by partial differential equations (PDEs). However, standard PINNs often struggle with convergence and accuracy, especially when solving non-linear PDEs. This difficulty is largely attributed to the static nature of traditional activation functions, which cannot dynamically adapt to the evolving topology of the loss landscape during training. To address these limitations, this work investigates the integration of adaptive activation functions within the PINNs framework…
- OpenAlex · Published · First archived
A Field-Calibrated Physics-Informed Digital Twin Framework for Production Blasting Using Dynamic Finite Element Modeling and Seismic Source Reconstruction ↗
Blast-induced ground vibration is traditionally predicted using empirical scaled-distance equations or numerical simulations driven by simplified analytical loading functions. Although dynamic finite element modeling has significantly advanced the understanding of stress-wave propagation, existing approaches remain unable to reproduce the complete seismic response of actual production blasting because the true blast source is generally unknown and is therefore replaced by simplified pressure–time functions. Consequently, a field-calibrated physics-informed Digital Twin framework for production…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
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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