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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 ↗309 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.
- arXiv · Published · First archived
Encoding Matters: A Qubit-Efficient Reformulation for Quantum Resilient Routing ↗
Qubits are powerful computational resources because each additional qubit doubles the size of the accessible state space. This expressive power comes at a cost: adding qubits also increases circuit size, constraint overhead, and exposure to hardware noise. These limitations are especially critical for near-term quantum computers, making the number of qubits required to formulate a problem a central practical concern. In this work, we present a method for efficiently mapping logistics and routing problems onto qubits through structure-aware encoding reduction. We demonstrate the method on a ben…
- arXiv · Published · First archived
Entanglement Catalysis Beyond the Asymptotic Rate ↗
Quantum entanglement is a fundamental resource in quantum information science, commonly consumed to perform tasks like quantum teleportation and quantum key distribution. Entanglement concentration converts partially entangled pure states into nearly perfect Bell pairs. While its efficiency is well understood in the asymptotic limit, practical protocols must operate with finite systems. In this finite regime, and at the low error levels relevant in practice, performance is penalized by corrections that reduce the achievable yield. We investigate whether this fundamental finite-size penalty can…
- Hacker News · Posted on Hacker News · First archived
Show HN: Physics written into the architecture (learned Hamiltonian) ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
An experimental tour-de-force: Entanglement between glass bead and light ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Physics-to-Math Research – an experimental Skill for scientific reasoning ↗
Hacker News discussion.
- OpenAlex · Published · First archived
Physics informed neural network framework for non-similar nano-heat and solutal transport in a rheological fluid over a stretching cylinder ↗
Accurate prediction of non-similar momentum, thermal and solutal transport phenomena in rheological fluids is crucial for the design and optimization of advanced thermal management systems, heat exchangers, polymer processing, chemical reactors, energy conversion devices and biomedical transport processes. Motivated by these engineering applications, this study presents a Physics-Informed Neural Network (PINN) framework for analyzing magnetohydrodynamic mixed convection flow of a Jeffrey fluid over a stretching horizontal cylinder embedded in a Darcy porous medium. The mathematical model incor…
- OpenAlex · Published · First archived
Prediction of Carbon Amount Based on Formation Rate in Catalyzed Methane Pyrolysis via Induction Heating: An Application of a Physics-Informed Neural Network ↗
Carbon deposition during catalytic methane pyrolysis under induction heating governs reactor operability and carbon recovery; however, the available datasets are sparse. In this study, literature-derived experimental data were curated and split into training, validation and test sets (70%, 15%, 15%) to develop physics-informed neural networks (PINNs) that predict the deposited carbon mass (MC) from the operating variables. Two models were proposed: PINN-M1, which embeds an Arrhenius-type carbon formation rate in the loss function, and PINN-M2, which constrains the time derivative of the MC to…
- OpenAlex · Published · First archived
Physics-regularized deep learning for mining method selection in computational geomechanics ↗
Selecting an appropriate mining method for fractured “three-soft” ore bodies, with soft ore, soft hanging wall, and soft footwall, is a safety-critical artificial-intelligence application in computational geomechanics. Existing multi-criteria decision-making (MCDM) methods are limited by subjective weighting, linear additive assumptions, and weak use of numerical simulation outputs. This study develops a Physics-Regularized Transformer (PRT) artificial-intelligence framework for underground mining method selection. The framework integrates type-aware tabular embedding, a physics-regularized at…
- OpenAlex · Published · First archived
A Physics-Constrained Probabilistic Forecasting Method for Wind–PV–Load Systems Under Extreme Weather Based on Extreme-Event Awareness and Uncertainty Calibration ↗
With the increasing penetration of renewable energy into power systems, wind and photovoltaic (PV) power generation exhibit substantial uncertainty due to meteorological variability, which becomes more pronounced under extreme weather conditions. The scarcity of extreme samples, complex dependencies among wind power, PV power, and load, and degraded prediction-interval reliability further increase the difficulty of probabilistic forecasting. To address these challenges, this study proposes a physics-constrained probabilistic forecasting framework for wind–PV–load systems under extreme weather.…
- OpenAlex · Published · First archived
Quantum Fisher information and photon statistics from shift current shot noise ↗
Quantum Fisher information (QFI) sets the ultimate precision of optical phase measurements and can reveal multiphoton entanglement. Yet the photon-number fluctuations that encode this sensitivity are difficult to convert into an electrical signal. We theoretically predict that a photodetector utilizing the shot noise of the quantum-geometric shift current of exciton polaritons can measure photon-number statistics and, for pure states, infer the QFI. By solving the Lindblad equation, we obtain the time-dependent nonlinear photocurrent for an arbitrary initial photon state. It turns out that, re…
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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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