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新论文 · v1arXiv:2611.14777

Mach2: Scientific Discovery Platform with Deterministic Execution Constraints

Vansh Sharma, Venkat Raman

原文摘要

Language-model agents can formulate workflows and invoke specialized tools, yet compliance with natural-language operating rules is not guaranteed by prompting alone. Mach2 addresses this limitation by coupling Eigen orchestration to deterministic, lint-like checks of tool requests, source changes, acceptance tests, and artifact contracts. Domain requirements are represented as executable predicates: mandatory failures prevent advancement of the checked operation, while diagnostic findings are retained for correction or review. User-specified test-driven development (TDD), CodeGrapher's abstract-syntax-tree analysis, and history-guided evolutionary search provide complementary verification and optimization mechanisms. Mach2 also generates task-specific agent packages for execution through independently selected endpoints. The originating harness validates returned artifacts against declared interfaces and evidence requirements without assuming continuous supervision of the remote run. Model-internal-knowledge (MIK) dependencies are recorded separately from source-grounded and test-supported findings. This paper formalizes the rule-gated execution model and the agent-package lifecycle, and proposes measurable criteria for evaluating rule compliance, engineering validity, and cross-endpoint task continuity.

cs.SE · cs.AI · physics.app-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14744

Beyond Ground States: Learning Many-Body Hamiltonians for p-Bit Invertible Arithmetic

Tian Feng, Xutang Fan, Yiyun Chen

原文摘要

Probabilistic-bit (p-bit) invertible logic encodes a Boolean relation as an energy whose ground states are its legal assignments, computing in any direction by sampling. Traditional constructions target ground-state membership and a wide minimum energy gap, yet finite-time samplers traverse excited states. We cast Hamiltonian design as inverse statistical mechanics: fit an energy with interactions up to order four whose Gibbs law approaches uniformity on each clamp case's legal states, under a gap floor keeping every legal state below every illegal one. Constrained maximum likelihood with pruning and refitting fits terms from a fixed pool, and every delivered energy is audited exactly on all cases. Each energy is scored by time to a verified solution under restarts with its best of nine tuned samplers. In underdetermined modes, learned multipliers factor in 0.55, 0.44, 0.52 and 0.46 [0.38, 0.56] of the hand-written encodings' updates from 4x4 to 7x7, faster within every family below 7x7 at no resolved work penalty; learned adders solve the reverse mode in 0.71 of the updates at 8 bits and about a quarter at 24 and 32 bits, at 0.55 of the work at 32 bits. At 5x5 under equal pool, budget and floor, a gap-maximizing fit reaches 0.79 [0.73, 0.88] in updates and 1.26 in work against the hand-written encoding, while likelihood fitting takes 0.55 [0.46, 0.58] and 0.58 [0.48, 0.61] of the gap fit's updates and work. Rounding coefficients to the narrowest exact integer words tested, we run both energies at each pair's common word on one FPGA color-sweep SSA core, bit-exact with software: learned multipliers take 0.41 of the hand-written encoding's cycles at 4x4 and 0.057 [0.045, 0.068] at 7x7, at up to 2.4 times its lookup tables. The method requires only a circuit's legal assignments; in determined modes, hand-written encodings take less work, and from 12 bits on the adder fewer updates.

cond-mat.dis-nn · cs.ET · physics.app-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14729

SunLightBots: Turning sunlight into self-sustained, high-power motion in soft robots

Yoo Jin Lee, Melanie Torres Crespo, Zhongxiao Deng, Andrew Tejada-Vega, Rebecca Kramer-Bottiglio, Wenzhong Yan, Yimin Luo

原文摘要

Soft robots built from active materials can directly convert environmental stimuli into motion, offering a route to energy autonomy and embodied control. However, environmental energy is generally harvested at low power, whereas rapid, mechanically demanding actions require high mechanical output power. Reconciling this mismatch while sustaining self-regulated cyclic actuation is challenging, particularly at the subgram scale, where limited payload capacity constrains the integration of conventional power and control hardware. Here, we present SunLightBots, a class of subgram, sunlight-powered, untethered soft robots that achieve autonomous locomotion under continuous illumination near 1 Sun (100 mW/cm2). Their operation is enabled by a self-resetting snapping (SRS) mechanism that integrates liquid crystal elastomers (LCEs) with snap-through instability to accumulate solar energy supplied at low power, release it as rapid, high-power motion, and reset through photothermal-mechanical feedback. Through coordinated optimization of the LCE actuation temperature, photothermal conversion, actuation stress, and structural energy barrier, the system operates near 1 Sun and achieves a peak mass-specific mechanical power of 89 W/kg. At this irradiance, the optimized LCE generates an actuation stress of 0.1 MPa at approximately 60 deg C. SunLightBots demonstrate sustained swimming, crawling, and repeated impulsive jumping. Under 1.5 Suns, the swimmer advances more than three body lengths per cycle. This work provides a materials-mechanics route toward autonomous soft robots powered entirely by the environment.

cs.RO · cond-mat.soft · physics.app-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12010

Scalable Bayesian Optimization for Efficient Exploration of Moire Patterns

Panagiotis Krokidas, Christos Lazaridis, Vassilis Sioros, Efi-Maria Papia, Vassilios Constantoudis, George Giannakopoulos

原文摘要

Large materials design spaces often contain rare high-performing configurations that are difficult to identify through trial-and-error exploration or purely representative machine-learning (ML) sampling. In this work, we present FruBO, a flexible Bayesian Optimization framework for accelerating the discovery of optimal configurations in computationally demanding materials design problems. As a case study, we investigate multilayer Moiré patterns generated by the superposition of six rotated hexagonal lattices, where the objective is to identify the rotational conformation that maximizes pore-size uniformity, quantified using the Jain fairness index. The resulting benchmark contains 600,000 unique configurations and exhibits a highly skewed objective distribution, making the global optimum difficult to locate by random sampling. We compare random exploration, Gaussian Process-based Bayesian Optimization, and Bayesian Optimization using a lightweight CatBoost surrogate with committee-based uncertainty estimation. Random sampling identifies the global optimum only after 533,012 evaluations, requiring approximately 31 hours, while Gaussian Process-based BO becomes impractical due to GPU memory exhaustion after 290 acquired samples. In contrast, FruBO with CatBoost identifies the global optimum after 1,470 acquired samples and approximately 1.15 hours. These results demonstrate that scalable surrogate models can substantially improve the feasibility and efficiency of BO in large nanomaterials design spaces.

cs.LG · physics.app-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11563

HydroTuring: A physics probe benchmark testing mass, energy, and momentum conservation in hydrologic models

Zhi Li, Changming Li, Siavash Shams, Han Wang, Qingyi Yang, Yuanhang Liu, Taiqi Lian, Xin Lan 等 20 位作者

原文摘要

Hydrologic models, including deep learning systems now deployed for operational flood forecasting, are evaluated primarily by how closely their simulated hydrographs match observed streamflow. However, high hydrograph skill does not guarantee physical consistency: in a synthetic calibration experiment, a conceptual model that loses 15% of its water input to an unreported sink still reaches a Nash-Sutcliffe efficiency of 0.93, and no shared benchmark tests whether models obey fundamental conservation laws. Here we introduce HydroTuring (v0.1.0), a black-box benchmark of 37 physics probes that test mass (24 probes), energy (7) and momentum (6) conservation in hydrologic models. Each probe runs a controlled experiment on forcing generated at run time from a recorded seed and applies binary pass or fail criteria validated against 18 physically exact and 59 deliberately broken reference models. Across 32 evaluated models (3 exact references, 7 AI-based models and 22 process-based models), only the 3 exact references pass every probe among the 28 models scored on 5 or more probes. Process-based and AI-based models pass similar shares of their scored probes (76% and 74%), and a pre-trained flood-forecasting LSTM fails 6 of the 11 runoff-only probes it can be scored on. Furthermore, 49% of all 1,184 model-probe pairs remain unscored because models omit the required state or flux variables, while the scored failures occur in perturbation responses and state bounds rather than long-term water-budget closure. HydroTuring provides a reproducible smoke test for model development, peer review and operational deployment under an open community protocol.

physics.geo-ph · cs.AI · cs.LG · physics.app-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.28200

Vibe-FDTR: An agent-oriented framework for reproducible frequency-domain thermoreflectance data analysis

Fuwei Yang, Weiheng Li, Bai Song

原文摘要

Frequency-domain thermoreflectance (FDTR) is a laser pump-probe technique widely used to measure thermal properties at the micro- and nanoscale; however, it relies on a complex data analysis procedure that demands substantial domain expertise and is susceptible to subtle human errors. Here, we present Vibe-FDTR, an agent-oriented framework that enables large language model (LLM) agents to perform reliable and reproducible FDTR analyses directly from natural language requests. This framework couples a configuration-driven FDTR code package, which enforces physical and parametric consistency, with procedural agent skills that translate user intentions into organized and verifiable analysis steps. We evaluate Vibe-FDTR using a controlled benchmark with two levels: synthetic single-step tasks and real-data multi-step tasks based on measurements of gold-coated graphite samples. Across the two levels, agents using Vibe-FDTR achieve success rates of 100% and 98.9%, respectively. In sharp contrast, ablating skills (Code-agent) reduces performance to 91.4% and 36.7%, which drops further to 38.6% and 0% when the domain package is also omitted (Agent-only). Beyond success rate, Vibe-FDTR also reduces computational cost by 87.7% relative to the Code-agent variant and cuts execution time by more than 60%. Finally, an optional expert mode supports experimental planning via autonomous sensitivity and uncertainty evaluations, and formulates physically grounded recommendations for underspecified tasks. These results demonstrate that encapsulating domain code and expert knowledge into agent skills offers a promising route toward low-barrier, autonomous, and trustworthy thermal metrology.

physics.app-ph · cond-mat.mtrl-sci · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.15215

Stochastic Ising networks with asymmetric and time-delayed interactions

Hantao Zhang, Sidra Gibeault, Matthew W. Daniels, Philippe Talatchian, Ursula Ebels, Advait Madhavan, Mark D. Stiles

原文摘要

Stochastic Ising networks are widely used to describe collective dynamics in complex systems and to compute, yet physical implementations of these networks often contain both asymmetric interactions and nonzero signal propagation times that fall outside conventional theories of their behavior. Here, we study stochastic Ising networks with asymmetric and time-delayed interactions motivated by experimental observations in coupled superparamagnetic tunnel junctions. Time delay fundamentally reshapes the dynamics induced by antisymmetric couplings, producing strong oscillatory temporal correlations consistent with experiment. Sufficiently long delays strongly suppress the non-uniformity of steady-state probability distributions, a behavior explained by an exponential suppression of the first-order equal-time cross-correlation between pairs of spins. Residual non-uniformity is due to the equal-time cross-correlation mediated by a third spin that contributes only at higher order in the coupling strength. These relatively featureless probability distributions coexist with pronounced delayed temporal correlations, distinguishing them from equilibrium high-temperature behavior. We further show analytically that these delay-induced near-uniform distributions emerge in a broad class of stochastic networks that lack symmetry-breaking bias fields. Introducing symmetry-breaking bias fields restores a strong dependence of the steady state on interactions with qualitatively modified behavior. Simulations of networks with five coupled spins demonstrate that these effects persist beyond minimal systems with only two spins.

physics.app-ph · cond-mat.mes-hall · cond-mat.stat-mech · cs.ET · physics.data-an

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.05572

Metamaterial tactile displays with programmable virtual pixels

Gregory Reardon, Max Linnander, Dustin Goetz, Neeli Tummala, Yon Visell

原文摘要

Tactile displays present information by supplying localized mechanical signals across their surface, but conventional approaches often require an actuator to be positioned at every location where feedback is provided, limiting resolution and increasing complexity. Wave-mediated tactile displays exploit wave transmission to generate virtual pixels using far fewer actuators, but are constrained in resolution by the intrinsic dispersion characteristics of the display medium. Here, we show how this constraint can be overcome by applying metamaterial physics to engineer wave transmission in a rigid plate using an optimized lattice of resonators. The resulting system exhibits a slow-wave dispersion branch that reduces the wavelengths available for constructing virtual pixels, thereby enhancing display resolution by an order of magnitude. We demonstrate a metamaterial tactile display driven by eight peripheral actuators that is capable of generating localized virtual tactile pixels at any surface location with centimeter-scale resolution. The location, timing, and waveform of each pixel can be specified computationally, and the output bandwidth, 75-400 Hz, spans a substantial portion of the tactile frequency range. In four perceptual experiments, participants localized, explored, and discriminated multi-pixel patterns comprising transient and sustained vibrations with accuracies ranging from 95.3% to 100%. This research provides metamaterial engineering methods that enable the design of tactile displays driven by few actuated degrees of freedom, with implications for many future application areas.

cs.ET · cs.HC · cs.RO · physics.app-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05959

Local information processing capacity: anatomy of computation in neuromorphic systems

Jiaxuan Chen, Daiki Nishioka, Yicheng Song, Ryo Iguchi, Sota Hikasa, Hina Kitano, Akira Hirose, Takashi Tsuchiya

原文摘要

Many neuromorphic systems possess rich internal computational structures shaped by their physical dynamics. However, existing metrics characterize only global computation, leaving these local structures unresolved despite their importance for system interpretation, optimization and miniaturization. Here, we introduce local information processing capacity (L-IPC), which represents individual dynamical states as vectors in an infinite-dimensional Hilbert space and decomposes their computational properties through subspace projections onto nonlinearity orders and memory steps, with the residual quantifying computational irrelevance. Applied to three systems with comparable global capacities (an echo state network, an ion-gating reservoir and a spin-wave reservoir), L-IPC reveals markedly different distributions of computation, ranging from homogeneous to spatially, temporally and spectrally structured patterns. These local profiles further guide task-specific pruning, outperforming standard approaches at the same size in the structured systems. These results establish L-IPC as a general framework for anatomizing computation and guiding task-adaptive design in neuromorphic systems.

cs.ET · physics.app-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03485

Identifying the Shear Piezoelectric Coefficient using Physics-Informed Neural Networks under Exact Boundary and Initial Constraints

Suhas Suresh Bharadwaj

原文摘要

The shear piezoelectric coefficient $e_{15}$ governs the shear-mode electromechanical coupling in piezoelectric micromachined ultrasonic transducers (PMUTs) and surface acoustic wave (SAW) devices, but measuring it typically requires destructive resonance testing on dedicated specimens. On simulated data, $e_{15}$ can instead be recovered non-destructively from sparse, noisy subsurface wavefield observations using a constrained physics-informed neural network (PINN). By building exact spatial distance functions and temporal multipliers directly into the network output, the clamped, grounded boundaries and resting initial state hold automatically, leaving the loss with only the interior equations of coupled electro-elastodynamics and the data misfit. Non-dimensional scaling brings all governing coefficients within one decade of O(1), and the setup removes the boundary and initial-condition penalty weights that soft-constraint methods must tune. In a six-way comparison (plane-strain PZT-5H, manufactured wavefield, 30 dB noise), a soft-penalty baseline with adaptive weights converges to an erroneous $e_{15}$ in four of the five runs (51.7% mean error), and fixed penalties reach an error of 0.85% only after a second-order optimization stage. The constrained formulations reach mean $e_{15}$ errors between 0.15% and 0.51% with held-out displacement errors below 1%. Within these variants, the spatial multiplier gives rapid first-order convergence, and the quadratic temporal multiplier lowers the early-time electrostatic residual by a factor of 6.8 versus a linear one. On finite-element data along one subsurface line, the full formulation recovers $e_{15}$ with 1.19% mean error at 30 dB and keeps it between 0.65% and 2.71% across a 40 to 0 dB noise sweep. Finally, training on displacements alone still recovers $e_{15}$ with mean errors of 0.45% on finite-element and 1.64% on manufactured data.

physics.comp-ph · cond-mat.mtrl-sci · cs.LG · physics.app-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.29362

Compositional neural networks for multimodal X-ray and neutron scattering inversion

Valentin Munteanu, Alexander Hinderhofer, Dmitry Lapkin, Vladimir Starostin, Frank Schreiber

原文摘要

X-ray and neutron scattering are indispensable for structural characterization across condensed-matter physics and materials science. Yet, because scattering experiments generally record intensities without phase information, distinct structures can yield similar scattering patterns, rendering the corresponding inverse problems non-unique. This ambiguity can be reduced by combining complementary measurements, but their integration into machine-learning frameworks remains challenging. Here we introduce a modular framework for probabilistic multi-measurement analysis based on prior-amortized flow matching. Single-observation posterior estimators are trained separately for individual observation types or physical probes and combined only at inference time through a compositional sampler. This late fusion approach allows pretrained single-observation models to be reused without dedicated joint retraining. As a case in point, we consider reflectometry, which provides depth-resolved, nanometer-scale information about thin films and interfaces. Our single-observation demonstrations span X-ray, time-of-flight neutron and polarized neutron reflectometry. In separate co-refinement studies, task-matched estimators enable homogeneous composition across multiple neutron contrasts and heterogeneous multimodal composition of X-ray and neutron measurements. Together, these results establish compositional flow matching as a modular approach to uncertainty-aware inversion and co-refinement, with potential applicability to other scattering problems in which multiple observations constrain a common physical structure while retaining measurement-specific parameters.

cs.LG · cond-mat.mtrl-sci · physics.app-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01675

Learned optical compression for single-pixel quantitative phase imaging

Ary Portes, Yuntian Wang, Che-Yung Shen, Shiqi Chen, Aydogan Ozcan

原文摘要

Quantitative phase imaging (QPI) reveals optical path-length variations in transparent objects but typically relies on spatially resolved intensity measurements. Here we numerically and experimentally demonstrate learned optical compression for single-pixel QPI, jointly optimizing a diffractive encoder and a digital neural decoder to concentrate reconstruction-relevant phase information into a compact measurement vector. A single reconfigurable phase-modulation plane sequentially displays the learned encoding patterns, which are independent of the input object, each yielding one scalar intensity measurement. Operating at a visible wavelength, the experimental system reconstructs quantitative phase images of unknown objects with ~40-fold dimensional compression, using only 2.55% as many intensity measurements as reconstructed pixels. Experimental blind testing on handwritten digits and grating-like phase objects achieves mean Pearson correlation coefficients of 0.945 and 0.848, respectively. Benchmarking under the same measurement budget demonstrates substantially higher reconstruction fidelity than other encoding strategies, highlighting the advantages of learned optical compression. With the optical encoder fixed, decoder-only neural adaptation enables experimental transfer to previously unseen data distributions, extending the utility of the learned measurement representation. These results establish learned optical compression as a measurement-efficient and reusable framework for QPI, integrating phase-information compression into single-pixel intensity measurements while retaining adaptability to new data. This approach paves the way for low-data-rate phase imaging and sensing, with the potential to extend QPI to spectral regions where high-quality detector arrays are costly or unavailable.

physics.optics · cs.CV · physics.app-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39297

Spreading Dynamics on Higher-Order Networks: A Structure-Mechanism-Behavior Perspective with Applications

Ming Xie, Shibo He, Qihao Huang, Yuzhen Fan, Jiming Chen

原文摘要

Spreading in real-world networks, including modern communication and networked systems, often unfolds through group interactions rather than isolated pairwise contacts. Higher-order network representations such as simplicial complexes, hypergraphs, and their temporal, multilayer, and directed extensions provide a principled way to encode group interactions. Unfortunately, existing studies on spreading over these substrates remain fragmented and largely model-centric, offering limited insight into how higher-order representations and contagion mechanisms jointly shape emergent dynamics. This review addresses this gap by introducing a structure-mechanism-behavior three-level perspective that provides a systematic mapping from higher-order network structures and spreading mechanisms to collective behaviors. Within this framework, we compare how different higher-order network representations encode group interactions, introduce a taxonomy of contagion mechanisms, and analyze their structural carrying capacity, defined as the ability of a given representation to support specific contagion mechanisms and the associated dynamical behaviors. We further map specific structure-mechanism combinations to three hallmark emergent behaviors, thereby offering a unified cross-level perspective beyond existing model-centric surveys. These insights are particularly relevant for communication systems involving group interactions, such as social information spreading, cooperative protocols, and distributed networked processes. Lastly, leveraging this three-level perspective, we offer a roadmap for guiding and controlling dynamics on higher-order networks, while discussing future directions in representation choice, mechanism design, and data-driven inference.

cs.SI · physics.app-ph

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arXiv ↗PDF ↗