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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 ↗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
Three-dimensional cementing displacement efficiency prediction based on domain-decomposed physics-informed neural network ↗
Efficient displacement of drilling fluid by cement slurry is essential for ensuring cementing quality, and accurate flow-field prediction is crucial for improving displacement efficiency. Although computational fluid dynamics (CFD) is widely used to investigate displacement mechanisms, high-fidelity three-dimensional simulations are computationally expensive and inefficient for rapid engineering prediction and optimization. In recent years, data-driven neural networks have attracted attention due to their high computational efficiency; however, they rely heavily on large datasets, are prone to…
- OpenAlex · Published · First archived
Perceived digital competence following virtual reality-supported physics learning in junior secondary schools ↗
Abstract Virtual reality-supported physics learning is increasingly recognized as a promising approach for strengthening both science understanding and digital competence in school settings. This study described students’ perceived digital competence after participation in virtual reality-supported solar system learning across four junior secondary schools. Using a quantitative post-intervention design, the study involved 144 students, with 36 students drawn from each school, who completed the 25-item Assessment of Digital Competence for Students (ADI-S), a self-report questionnaire covering o…
- arXiv · Published · First archived
Physics-based logarithmic description of electrostatic field enhancement in hemisphere-on-cylindrical-post structures for high-field applications ↗
Electrostatic (ES) field enhancement at sharp conducting structures plays a central role in lightning protection, corona discharge, electrical breakdown in vacuum (for example in particle accelerators), and more generally in technological applications of field electron emitters. A canonical geometry for studying this ES effect is the hemisphere-on-cylindrical-post (HCP) model, in the regime where the structure stands on a planar conductor of large lateral extent, and a large gap exists between the structure and the counter-electrode. A parameter of major interest is the apex field enhancement…
- Hacker News · Posted on Hacker News · First archived
Ask HN: How do you get your hands on physics environments for your robotics SIM? ↗
Are you building them procedurally yourself, or buying and rigging them, or using some other custom pipeline? Please shed light.
- Hacker News · Posted on Hacker News · First archived
Naïve physics ↗
Hacker News discussion.
- OpenAlex · Published · First archived
Single‐Shot Simultaneous Recovery of Phase and Degree of Polarization in Multimode Fibers via Physics‐Constrained Global Attention ↗
ABSTRACT Concurrently recovering the vectorial optical field, including the spatial phase and degree of polarization at the distal end of a multimode fiber using interferometer‐free measurements remains an open challenge. Existing computational approaches typically retrieve phase or polarization separately, as jointly decoding both quantities from a single scalar measurement constitutes a highly ill‐posed. Here, we demonstrate single‐shot simultaneous reconstruction of phase and degree of polarization from a single non‐interferometric speckle intensity frame. A dual‐path spatial‐multiplexing s…
- OpenAlex · Published · First archived
Physics-informed unsupervised battery state-of-charge estimation via temperature-conditioned Mamba and parallel Bayesian inference ↗
In this paper, we propose Mamba-IPLS, a physics-informed, unsupervised algorithm for offline, label-free battery state-of-charge (SOC) trajectory reconstruction. The architecture integrates a Mamba backbone with a Temperature-Conditioned Modulator (TCM) to generate model parameters under different ambient temperature conditions. For a sequence of length N , this Mamba–TCM neural parameter generator requires O ( N ) total work. The generated parameters drive a physical state–space model, where the latent battery states are estimated using a Parallel Iterated Posterior Linearization Smoother (IP…
- OpenAlex · Published · First archived
Linking DC transport properties and entanglement negativity in non-Hermitian superlattices ↗
Abstract We explore how non-Hermitian effects including gain–loss imbalance, asymmetric hopping, and complex potentials in one-dimensional superlattices influence both electrical transport properties and quantum correlations. Focusing on a multiband tetramerized superlattice, we study the impact of distinct gain–loss configurations on electrical transport coefficients such as the Drude weight, alongside mixed-state quantum entanglement measures. Using a class of generalized Su–Schrieffer–Heeger (SSH) Hamiltonians with adjustable sublattice asymmetry and non-reciprocity, we systematically evalu…
- OpenAlex · Published · First archived
A numerical method for coupling parameterized physics-informed neural networks and FDM for advanced thermal-hydraulic system simulation ↗
Severe accident analysis with system codes such as MELCOR is indispensable for nuclear safety assessment, yet the cost of repeated simulations constrains parametric studies and uncertainty quantification. Data-driven surrogates accelerate these analyses but require large simulation datasets, whereas physics-informed neural networks (PINNs) require no training data yet must be retrained for every parameter change and lose accuracy over long horizons. This study addresses both limitations with the P2F method — Parameterized PINNs coupled with the finite difference method (FDM) — for MELCOR’s Con…
- OpenAlex · Published · First archived
Decoherence-protected entangling gates in a silicon carbide quantum node ↗
Solid-state color centers are promising candidates for nodes in quantum network architectures. However, realizing scalable and fully functional quantum nodes-comprising both processor and memory qubits with high-fidelity universal gate operations-remains a central challenge in this field. Here, we demonstrate a fully functional quantum node in silicon carbide (SiC), where electron spins act as quantum processors and nuclear spins serve as quantum memory. Specifically, we design a pulse sequence that combines dynamical decoupling with hyperfine interactions to realize decoherence-protected univ…
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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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