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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 ↗318 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
Energy-reserve coordinated scheduling of cascade hydro-photovoltaic-pumped storage systems based on physics-constrained graph neural networks ↗
Intraday scheduling of cascade hydro-photovoltaic-pumped storage systems requires joint coordination of energy dispatch, photovoltaic accommodation, reserve security, and physical feasibility under photovoltaic fluctuation, load variation, inflow uncertainty, and limited storage capacity. Existing optimization methods can describe physical constraints, but their repeated solution in rolling scheduling depends on forecast inputs, uncertainty sets, or scenario construction. Data-driven dispatch models provide fast inference, yet generic temporal or graph structures may mix hydrological transfer,…
- OpenAlex · Published · First archived
Physics-Informed Explainable Machine Learning for Hybrid State of Health and Remaining Useful Life Estimation of Lithium-Ion Batteries ↗
Accurate health assessment is essential for the reliable and cost-effective operation of lithium-ion battery energy storage systems. This study presents a physics-informed explainable machine-learning framework for hybrid State-of-Health (SoH) estimation and Remaining Useful Life (RUL) prediction under heterogeneous simulated operating and degradation conditions. A multi-cell degradation model represents capacity fade, resistance growth, thermal and operational stress, and nonlinear aging acceleration. Hybrid SoH combines capacity- and resistance-related health information, while end of life (…
- OpenAlex · Published · First archived
Hybrid Physics-Informed Machine Learning via residual fitting for the prediction of creep and shrinkage in concrete ↗
Abstract Recent works about data-driven analysis of concrete creep and shrinkage have enhanced structural analysis, providing more efficient results compared to standard models. Moreover, recent studies have shown strong efficiency when physics laws are coupled with machine learning (ML), an approach called physics-informed machine learning (PIML). In this paper, the purpose is to implement Hybrid PIML via residual fitting to enhance standard ML models for predicting creep and shrinkage in concrete structures. While purely data-driven ML models offer high accuracy, they frequently lack physica…
- OpenAlex · Published · First archived
Predicting the post-fire load-bearing capacity of slab-column structures based on a physics-embedded deep learning algorithm ↗
While reinforced concrete slab-column structures have been widely adopted in practical buildings, this type of structure is vulnerable to brittle punching shear failure under fire. Predicting the post-fire structural capacity is of great importance to accelerate the post-fire retrofitting or replacement. However, the post-fire performance of slab-column structures is influenced by multiple factors and their interactions. This study proposes a physics-embedded deep learning algorithm to predict the load-bearing capacity of slab-column connections after fire. First, 108 scenarios considering the…
- OpenAlex · Published · First archived
Interface-aware adaptive layer-wise physics-informed neural networks for multilayer large-strain consolidation: a benchmark of architectures, training strategies, and trade-offs ↗
Multilayer large-strain consolidation in stratified soils involves nonlinear pore-pressure dissipation, evolving deformation, and material discontinuities, which pose significant challenges for physics-informed neural networks (PINNs). This study develops a layer-wise adaptive PINN framework in which soil layers are represented by neural subnetworks coupled through pore-pressure and Darcy-flux continuity conditions. A controlled three-layer benchmark is used to evaluate MLP and KAN architectures, adaptive activation, adaptive weighting, and parameter sharing over three independent random seeds…
- OpenAlex · Published · First archived
Efficiency, Curvature, and Complexity of Quantum Evolutions for Qubits in Nonstationary Magnetic Fields ↗
In optimal quantum-mechanical evolutions, motion can take place along paths of minimal length within an optimal time frame. Alternatively, optimal evolutions may occur along established paths without any waste of energy resources and achieving 100% speed efficiency. Unfortunately, realistic physical scenarios often lead to less-than-ideal evolutions that demonstrate suboptimal efficiency, nonzero curvature, and a high level of complexity. In this paper, we provide an exact analytical expression for the curvature of a quantum evolution pertaining to a two-level quantum system subjected to vario…
- OpenAlex · Published · First archived
Learning Quantum Hamiltonians at Any Temperature in Polynomial Time ↗
Abstract. We study the problem of learning a local quantum Hamiltonian [Formula: see text] given copies of its Gibbs state [Formula: see text] at a known inverse temperature [Formula: see text]. Anshu et al. [2020 IEEE 61st Annual Symposium on Foundations of Computer Science, pp. 685–691] gave an algorithm to learn a Hamiltonian on [Formula: see text] qubits to precision [Formula: see text] with only polynomially many copies of the Gibbs state, but which takes exponential time. Obtaining a computationally efficient algorithm has been a major open problem [ Alhambra, PRX Quantum, 4 (2023), 0402…
- arXiv · Published · First archived
Boundary-accessible work in a collision-charged topological quantum battery ↗
Quantum batteries store energy in nonequilibrium quantum states. The ergotropy sets the work extractable by an arbitrary global unitary, but a device usually exchanges energy through a single local port. It remains unclear how much stored work such a port can deliver, and whether topological edge modes help. We study a dimerized spin chain of Su-Schrieffer-Heeger (SSH) type. Excited two-level ancillas charge it at one end by repeated collisions, and an initially empty two-level load discharges it at the same site. An exact free-fermion mapping yields a bound on the load work for all number-con…
- arXiv · Published · First archived
Quantum information processing under maximal distance measures ↗
The measures of distance frequently used in quantum information, such as trace distance or fidelity-based purified distance, are the smallest extensions of the corresponding distance measures in classical information theory. However, this way of extending classical distances is not unique, and a whole spectrum of other extensions can be defined. Here we study the question of how quantum information processing tasks are affected by a change of the error metrics to the maximal extensions of the classical distance measures, imposing more demanding error requirements on operational tasks. We find…
- arXiv · Published · First archived
A Spectrum-Based Converse for Quantum State Discrimination and Its Applications to Classical-Quantum Channel Coding ↗
We investigate converse bounds on the average decoding error probability in finite-blocklength classical-quantum channel coding. We first present a lower bound for multiple quantum hypothesis testing in terms of pairwise trace distances and derive a corresponding fidelity bound. We then obtain a spectrum-based converse that depends only on the a priori probabilities and spectra of the states. We show that the converse bound remains tight for the quantum depolarizing channel under suitable conditions. For codes with product-state outputs, this converse takes an explicit form involving products…
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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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