THE INTERNET IS THINKING
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 ↗314 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 multiscale physics-informed neural network with RVE homogenization using isogeometric analysis for hygrothermal-electro-mechanical coupled analysis of piezoelectric composites ↗
Physics-informed neural networks have shown potential for coupled field analysis, but their application to heterogeneous piezoelectric composites under combined thermal and moisture effects remains limited because the effective properties depend on the microstructure and the residuals of the coupled governing equations may differ substantially in scale. This paper develops a multiscale method for hygrothermal-electro-mechanical coupled analysis of piezoelectric composites. At the microscale, a representative volume element of a 1–3 piezoelectric composite is constructed, in which the cylindric…
- OpenAlex · Published · First archived
Physics-Guided Neural Phase Demodulation for Non-Contact Laser-Ultrasound Sensing of Corneal Phantom Deformation ↗
Non-contact detection of weak, transient acoustic signals is essential for photoacoustic and laser-ultrasound (LUS) bioimaging, including the assessment of corneal deformation and thickness variation. Passive 3 × 3 fiber-coupler interferometers provide phase-diverse sensing without external modulation; however, conventional ellipse-based calibration becomes unreliable when the measured phase excursion covers only a small fraction of the characteristic ellipse, as in nanometer-scale corneal responses. This study presents a physics-guided temporal convolutional network (PG-TCN) that directly map…
- OpenAlex · Published · First archived
A collaborative multi-agent architecture with physics-consistency validation for robust microseismic signal classification ↗
Reliable classification of microseismic (MS) records is essential for geohazard early warning in deep underground construction. Corrupted or missing local observations can distort shared multi-sensor representations, and waveform similarity alone is often insufficient to separate engineering disturbances from MS events. This study proposes Physics-Informed Multi-Agent Reinforcement Learning (PI-MARL), a sensor-bound framework for classifying MS events, blasting signals, and interference signals. Each sensor channel is processed by an independent agent comprising a lightweight multi-scale 1D-CN…
- OpenAlex · Published · First archived
From Numerical Schemes to Neural Network Architectures: A Physics-Informed Framework for Differential Equations ↗
Physics-informed neural networks usually incorporate differential equations primarily through the loss function, while the neural architecture itself is selected independently of the underlying numerical structure. This work develops a scheme-induced approach in which an analytically modified numerical method defines the computational architecture, while selected elements of the numerical scheme are made trainable and refined using physics-informed loss functions. The specific contribution is the formulation and systematic investigation of a class of scheme-parameterized low-fidelity physics-i…
- OpenAlex · Published · First archived
Autoencoder-based non-intrusive model order reduction for anisotropic and multi-physics problems ↗
Abstract We propose a non-intrusive, Autoencoder-based framework for reduced-order modeling in continuum mechanics. Our method integrates three stages: (i) an unsupervised Autoencoder compresses high-dimensional finite element solutions into a compact latent space, (ii) a supervised regression network maps problem parameters to latent codes, and (iii) an end-to-end surrogate reconstructs full-field solutions directly from input parameters. To overcome limitations of existing approaches, we propose two key extensions: a force-augmented variant that jointly predicts displacement fields and react…
- OpenAlex · Published · First archived
Generalised Bit-Vector Abstractions for Formal Verification of Quantum Error-Detection and Entanglement Circuits over $$\{H,X,{\text{C-NOT}}\}$$: CSS Constructions, Soundness, and Mutation-Based Validation ↗
Abstract Formal verification of quantum circuits becomes increasingly difficult as circuit size increases, since exhaustive reasoning over the underlying Hilbert space is computationally expensive. In previous work [1], we introduced a bit-vector abstraction that separates the tracking of superposition from basis-state behaviour, enabling SMT-based verification of fixed-size error-detection and entanglement circuits constructed from H , X , and C-NOT gates. This paper extends that framework in five directions. First, we generalise the correctness properties to parametrised repetition and conca…
- OpenAlex · Published · First archived
CrossBlend physics-informed LSTM for nonlinear structural response prediction within a cyber-physical framework ↗
This study develops and evaluates physics-informed deep learning models for predicting nonlinear seismic structural responses within a cyber-physical framework. Four variants of Physics-Informed Long Short-Term Memory (PhyLSTM) models are proposed: baseline windowed and full-sequence PhyLSTM models and their CrossBlend counterparts. The CrossBlend mechanism implements a learnable dual-path displacement blending mechanism within the PhyLSTM framework, in which a sequence-level and floor-wise gating parameter adaptively combines direct displacement prediction with a velocity-integrated displacem…
- OpenAlex · Published · First archived
Physics-Guided Machine Learning for Joint Fault Diagnosis in Inverter-Fed Induction Motor Drives ↗
In intelligent electric vehicle (EV) traction drives, pulse-width modulation and field-oriented control (FOC) introduce broadband harmonics and non-stationary start-up transients. These dynamics obscure incipient inter-turn stator faults (ITSF), particularly when inverter switch failures and measurement interference co-occur. To meet the low-latency requirements of on-board vehicle control units (VCUs), this paper proposes a data-centric, physics-guided diagnosis pipeline. Relying exclusively on current measurements, the framework jointly detects and localises ITSF (1–3% severity), open-circui…
- OpenAlex · Published · First archived
Engineering Carbon Nanotube Quantum Well Defects With Recognition Tripeptides for Optical Detection of Extracellular Vesicles in Plasma ↗
ABSTRACT Extracellular vesicles (EVs) carry surface proteins that reflect the molecular signatures of their originating cells. However, their low abundance and the modest sensitivity of current EV detection methods in complex biological environments limit access to EV surface biology in the native biological context. Here, we present a near‐infrared optical nanosensor comprising a quantum well defect functionalized carbon nanotube coupled with integrin‐recognition RGD tripeptide to detect surface integrins on EVs in a human plasma background. The nanosensor achieved sub‐picomolar detection of…
- OpenAlex · Published · First archived
A comparative investigation of physics-driven machine learning architectures for consolidation analysis ↗
Physics-driven machine learning has emerged as a powerful framework for forward and inverse modeling of partial differential equations (PDEs), with increasing applications in geotechnical engineering. However, the relative performance of different physics-driven learning strategies remains problem-dependent, and their suitability for typical geotechnical problems has not been systematically established. This study systematically compares four physics-driven machine learning paradigms for consolidation analysis by combining Multi‑Layer Perceptrons (MLPs) and Kolmogorov–Arnold Networks (KANs) wi…
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Related threads
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.