Papers

Predicting flows that occur both through and around porous bodies is challenging due to coupled physics across fluid and porous regions and the need to generalize across diverse geometries and boundary conditions. We address this problem using two Physics Informed learning approaches: Physics Informed PointNets (PIPN) and Physics Informed Geometry Aware Neural Operator (P-IGANO). We enforce the incompressible Navier Stokes equations in the free-flow region and a Darcy Forchheimer extension in the porous region within a unified loss and condition the networks on geometry and material parameters. Datasets are generated with OpenFOAM on 2D ducts containing porous obstacles and on 3D windbreak scenarios with tree canopies and buildings. We first verify the pipeline via the method of manufactured solutions, then assess generalization to unseen shapes, and for PI-GANO, to variable boundary conditions and parameter settings. The results show consistently low velocity and pressure errors in both seen and unseen cases, with accurate reproduction of the wake structures. Performance degrades primarily near sharp interfaces and in regions with large gradients. Overall, the study provides a first systematic evaluation of PIPN/PI-GANO for simultaneous through-and-around porous flows and shows their potential to accelerate design studies without retraining per geometry.

Non-fullerene acceptors (NFA), such as Y6, have significantly improved the efficiency of organic photovoltaic devices (OPVs). However, the fundamental processes behind the high efficiencies of NFA devices have remained incompletely understood, with the high efficiencies persisting without the large energetic offsets often thought to be required for charge separation. Even more surprising has been the efficient charge generation in neat Y6 devices, where there is no energetic offset at all. Here, we simulate charge transport and separation in Y6 using delocalised kinetic Monte Carlo (dKMC) parameterised using atomistic calculations, thus taking into account the often-neglected ingredients of delocalisation, disorder, and polaron formation. Including delocalisation predicts higher carrier mobilities and exciton diffusion coefficients than is possible with classical simulations, bringing them into agreement with experimental values. Delocalisation also predicts higher charge-generation efficiencies in neat Y6, in agreement with experimental measurements. Finally, this work establishes dKMC as a realistic, predictive tool for understanding next-generation OPVs.

In Japan, severe rice shortages in 2024 sparked widespread public controversy across both news media and social platforms, culminating in what has been termed the "Reiwa Rice Riot." This study proposes a framework to analyze the temporal dynamics and causal interactions of emotions expressed on X (formerly Twitter) and in news articles, using the "Reiwa Rice Riot" as a case study. While recent studies have shown that emotions mutually influence each other between social and mass media, the patterns and transmission pathways of such emotional shifts remain insufficiently understood. To address this gap, we applied a machine learning-based emotion classification grounded in Plutchik's eight basic emotions to analyze posts from X and domestic news articles. Our findings reveal that emotional shifts and information dissemination on X preceded those in news media. Furthermore, in both media platforms, the fear was initially the most dominant emotion, but over time intersected with hope which ultimately became the prevailing emotion. Our findings suggest that patterns in emotional expressions on social media may serve as a lens for exploring broader social dynamics.

I discuss the physical basis of classical mechanics, such as expressed commonly using the framework of Newton's Principia. Newton's formulation of the laws of motion is seen to have quite a few ambiguities and shortcomings. Therefore I offer an alternative set of laws, based in particular on ideas of his contemporaries Huygens and Leibniz with a crucial addition by Ludwig Lange, which avoids the problems with Newton's formulation. It is shown that from these laws of motion all the usual results of classical mechanics, as it concerns the motion of idealized point masses, can be rederived. The application of these principles to relativistic point particles is discussed.

Machine-learning potentials (MLPs) have become important tools for modern molecular simulations. However, developing models that simultaneously achieve high accuracy and high computational efficiency remains a significant challenge. In this work, we extend the recently proposed MB-PIPNet framework to general covalently bonded molecular systems by combining monomer-based energy decomposition, permutationally invariant polynomial (PIP) descriptors, and neural networks within a fragmentation-based strategy. Within this framework, the total potential energy is represented as a sum of effective monomeric contributions, where PIPs provide compact and chemically motivated descriptions of both monomer internal structures and their local chemical environments. As a proof-of-concept application, we apply the MB-PIPNet framework to linear alkanes, using n-Tetradecane as a representative system, and benchmark its performance against established atomistic machine-learning models. The resulting MB-PIPNet potential accurately reproduces reference ab initio electronic energies and reliably captures key molecular properties, including torsional potential energy profiles, harmonic vibrational frequencies, and vibrational power spectra obtained from molecular dynamics simulations. Importantly, MB-PIPNet demonstrates a substantial advantage in computational efficiency over other MLP models for combined energy and force evaluations. These results establish MB-PIPNet as a scalable and efficient framework for constructing MLPs, providing an additional route for large-scale quantum and classical simulations of complex molecular systems.

Adjoint-based data assimilation for turbulent Navier-Stokes flows is limited by backward adjoint growth and increasing dominance of small-scale structures, which degrade reconstruction of initial conditions from sparse measurements. We show that the relative weighting of spectral components can be systematically controlled by redefining the inner product under which the adjoint operator is defined. The resulting Fourier-space weighting kernel acts as a preconditioner for the optimization. Specific kernels correspond to fractional integration or diffusion operators on the initial condition. Numerical experiments show that flow-dependent kernel selection substantially improves reconstruction stability and accuracy: exponential kernels suppress high-wavenumber contributions, whereas a fractional integral kernel is particularly effective for forced Kolmogorov flow. Ensemble statistics of adjoint fields reveal scale-dependent backward growth rates, explaining the instability of the standard formulation and how spectral preconditioning attenuates incoherent small-scale amplification.

Spectral and spatial fringes in polarized light are produced by the interference of transmitted and reflected waves at the interface between materials with different indexes of refraction. These instrumental artifacts can affect the accuracy of optical designs conceived for high-sensitivity spectroscopy and polarimetry. We consider the principal sources of these artifacts and the possible design pathways to mitigate them. In order to do so, we have developed an approximate yet agile treatment of the problem of the transmission and reflection of light in birefringent materials, which fundamentally relies on the assumption of small birefringence of the modeled materials for its implementation. The comparison of our results with those from more rigorous treatments, such as Berreman calculus, thus also serves as a test of the limits of the small-birefringence approximation in optical design applications. The treatment presented in this work is limited to isotropic materials and uniaxial crystals, which are the most common types of optics employed in polarimetric instrumentation. An extensive set of modeling examples is provided to illustrate the salient characteristics of polarization fringes and their dependence on optical design parameters.

A high-resolution beam telescope is essential for the precise characterization of silicon pixel sensors. As part of the CSNS-II upgrade project, a High-Energy Proton Beam Telescope (HEPTel) based on monolithic active pixel sensors (MAPS) has been designed for the forthcoming High-Energy Proton Experimental Station (HPES), which will provide 0.8 to 1.6 GeV single-particle proton beams. HEPTel consists of six ultra-thin telescope modules, with a material budget per module of about 0.061% X0. Simulated with a 1.6 GeV proton beam, the telescope is expected to achieve a resolution of about 1.83 micrometers. Additionally, a dedicated readout electronics system and a Data Acquisition (DAQ) system have been designed for HEPTel, based on which a preliminary test system was established for beam tests. The beam test results with 1.3 GeV electrons demonstrated a single-module resolution of about 5.77 micrometers, an overall telescope resolution of about 2.70 micrometers, and a detection efficiency above 99.5%. These results validate the HEPTel design and confirm its capability for forthcoming proton-beam experiments at HPES.

Light detection and ranging (LiDAR) systems are pivotal for precise distance and velocity measurement, yet widespread deployment requires solutions that balance their performance, robustness, and simplicity. Here, we propose a novel chaotic LiDAR system based on a semiconductor vertical-cavity surface-emitting laser (VCSEL) with delayed orthogonal polarization feedback. By exploiting the intrinsic competition between the transverse electric (TE) and transverse magnetic (TM) modes, the system generates a polarization-multiplexed dynamics: a chaotic TM mode serves as the reference, while a feedback-modulated TE mode probes the target. This all-in-one source eliminates the need for external optical modulators or complex coherent detection. The system's dynamics is finely tunable via a half-wave ($\lambda$/2) plate in the feedback loop and the laser injection current, enabling real-time optimization of the cross-correlation signal-to-noise ratio. Experimental results demonstrate precise linear ranging with a resolution of approximately 1.2 cm. Furthermore, the system exhibits strong inherent resistance to external optical interference, maintaining accurate ranging even in the presence of a secondary laser source. This compact, tunable, and interference-resilient platform offers a promising pathway toward low-cost, high-performance LiDAR for applications in autonomous navigation, robotics, and industrial metrology.

The nucleation of quasicrystals remains a fundamental puzzle, primarily due to the absence of a periodic translational template. Here, we demonstrate that phasons - hidden degrees of freedom unique to quasiperiodic order - drive diverse nucleation pathways in icosahedral quasicrystals (IQCs). Combining a Landau free-energy model with the spring pair method, we compute distinct critical nuclei and their corresponding minimum energy paths. At low temperatures, a direct, symmetry-preserving pathway dominates. In contrast, higher temperatures promote a "symmetry detour" that reduces the nucleation barrier via a lower-symmetry critical nucleus. Remarkably, while the resulting bulk IQCs exhibit distinct real-space symmetries, they remain thermodynamically degenerate with identical diffraction patterns. We resolve this paradox within the high-dimensional projection framework, showing that phason shifts modulate real-space symmetry without altering bulk thermodynamics. Our findings establish phasons as the structural origin of pathway diversity, offering a new physical picture for the emergence of quasiperiodic order.

Moiré patterns have recently become a very active field in nanophotonics. Those structures exhibit novel photonic properties unattainable with traditional photonic crystals. Especially, moiré magic configurations have been shown to allow intriguing slow light modes with zero group velocity. Starting from macroscopic moiré patterns in the everyday life, we will then shift to the subwavelength scale of moiré photonic crystals and detail some of their unusual properties.

15 years ago Dmitry Diakonov wrote the paper "Towards lattice-regularized Quantum Gravity", arXiv:1109.0091. In his approach, gravity with metric and tetrads arise from pre-geometric quantum fields leading to unusual dimensions of physical quantities. In particular, particle masses are dimensionless. We are trying to extend the Akama-Diakonov-Wetterich theory by introducing the Planck constants $\hbar$ and ${/\!\!h}=\hbar c$ as elements of the emergent metric. The inverse Planck constant $1/\hbar$ has the dimension of frequency, and, therefore, the mass $M$ of a particle, which has the dimension $\hbar\omega$, is dimensionless. In this extension, quantum mechanics emerges from the intrinsic quantum fields either in the symmetry breaking mechanism (GUT), or in the opposite mechanism of emergent symmetry in the low-energy corner (anti-GUT). In both cases, quantum mechanics (QM) serves as a bridge between the area of quantum fields (QFT) in the limit $1/\hbar \rightarrow 0$, and the area of classical physics (CM) in the limit $\hbar \rightarrow 0$. In the GUT scheme the inverse Planck constants, $1/\hbar$ and $1/{\\!\!h}$, play the role of the order parameter of the symmetry breaking phase transition from the pre-geometric QFT state to the QM state, in which the quantum mechanics emerges together with the space-time metric. In this phase transition, the integration over field variables in the QFT phase transforms to a path integral formulation of QM, which in turn yields the laws of classical mechanics in the limit $1/\hbar \rightarrow \infty$.

This work presents a novel strategy to address Navier-Stokes modelling errors arising on first-nearest neighbour lattice Boltzmann (LB) methods and introduces fully local corrections through Onsager-Regularized (OReg) non-equilibrium populations. The proposed mechanism, which admits partially and completely corrected OReg models, is used to develop representative partially and completely corrected models for the six-moment-constrained guided equilibrium (GEq) representation on the D2Q9 lattice. The former realization only addresses compatibility condition violations and improves the accuracy by two/four orders of magnitude at reference/arbitrary lattice temperatures respectively, while the latter additionally corrects stress tensor modelling errors, resulting in a fully corrected exact model. Numerical benchmarks of the corrected schemes demonstrate improved accuracy and stability in comparison to the Lattice-BGK, regularized, and uncorrected OReg-GEq schemes thus presenting a promising avenue for OReg based thermohydrodynamic extensions.

Travel time prediction is central to transport geography and planning's accessibility analyses, sustainable transportation infrastructure provision, and active transportation interventions. However, calculating accurate travel times, especially for driving, requires either extensive technical capacity and bespoke data, or resources like the Google Maps API that quickly become prohibitively expensive to analyze thousands or millions of trips necessary for metropolitan-scale analyses. Such obstacles particularly challenge less-resourced researchers, practitioners, and community advocates. This article argues that a middle-ground is needed to provide reasonably accurate travel time predictions without extensive data or computing requirements. It introduces a free, open-source minimally-congested driving time prediction model with minimal cost, data, and computational requirements. It trains and tests this model using the Los Angeles, California urban area as a case study by calculating naive travel times from open data then developing a random forest model to predict travel times as a function of those naive times plus open data on turns and traffic controls. Validation shows that this interpretable machine learning method offers a superior middle-ground technique that balances reasonable accuracy with minimal resource requirements.

Can geometry alone reshape equilibrium? Cascaded nanofluidic chambers show complex accumulation patterns, traditionally attributed to geometric diode effects. We use 3D molecular dynamics to decouple funnel rectification from boundary reflection. Simulations with argon parameters (r = 0.19 nm) reveal a striking "reverse" rectification in a 2-chamber setup: the narrow side accumulates over 5x more particles (N_1/N_0 = 5.37 +/- 0.01, p < 0.0001). In a 10-chamber argon cascade, this effect drives massive downstream accumulation. A symmetric control (w_L = w_R) eliminates the gradient, confirming that funnel asymmetry - not boundary/edge effects - is the primary driver in the ballistic regime. By contrast, the super-atom regime is dominated by boundary reflection. Our results challenge standard entropic transport theory and provide design rules for passive, geometry-driven density gradients - no pump, no drive.

We develop a statistical mechanics model for spatially extended epidemic dynamics in which contagion is mediated by an explicit environmental field rather than by instantaneous local contact. Building on the Doi Peliti formalism, we construct a stochastic field theory for susceptible and infected hosts coupled to a diffusive pathogen field and integrate out the mediator to obtain a non local effective action with a Yukawa type interaction kernel in space and memory effects in time. This formalism yields closed analytical expressions for the basic and effective reproduction numbers, including one loop fluctuation corrections that renormalize the transmission rate and generate spatial shielding effects beyond mean field SIR models. We derive an effective screening mass and a Debye like correlation length whose critical softening signals an impending instability of the disease free state, providing a field theoretical early warning indicator for epidemic waves. Superspreading is incorporated through a heterogeneous “epidemic charge“ distribution, which controls the divergence of the correlation length and explains the emergence of multifocal outbreak patterns even when the population average $R_0$ is subcritical. Finally, we apply the model to high resolution COVID19 incidence data across $\approx 400$ German districts, dynamically extracting the effective screening mass and the renormalized reproduction number, and show that the gauge mediated indicators anticipate major changes in epidemic regime several days before classical SIR based estimates.

The optical frequency comb has made a significant impact in precision spectroscopy and on our ability to probe atomic, molecular and, recently, nuclear transitions to further our understanding of their fundamental properties and how their dynamics and complex interactions affect the observed world. To expand the energy scales and types of systems that can be studied, frequency comb sources from terahertz to vacuum ultraviolet frequencies and beyond have been pursued. Dual-comb spectroscopy, enabled by the development of these frequency comb sources, allows broadband absorption measurements of complicated spectra, exceeding the limitations of direct, single-comb spectroscopy. To date, however, the dual-comb approach has not been able to directly access many important transitions that lie at challenging vacuum ultraviolet wavelengths. Here, we demonstrate dual-comb spectroscopy in the vacuum ultraviolet utilizing intracavity high harmonic generation. This multi-harmonic source is used to measure molecular absorbance spectra at $\lambda=210$~nm and $\lambda=149$~nm from room-temperature samples of acetylene and ammonia, respectively. These measurements resolve the Doppler broadened structure of congested molecular spectra with absolute frequency accuracy. Noise contributions to the vacuum ultraviolet dual-comb spectroscopy measurements are characterized, guiding future efforts and technological development in this region.

The extraction of spatio-temporal coherence in high-dimensional, chaotic, non-linear dynamical systems, such as turbulent flows, remains a fundamental challenge in physics, mathematics and engineering. In this work, we employ Shannon transfer entropy (TE) to identify causally coherent motions in a zero-pressure-gradient turbulent boundary layer (TBL). This causality metric, rooted in information theory, enables the identification of sources and targets in dynamical systems using the corresponding time series. However, TE requires sophisticated tuning of various hyperparameters, such as the Markovian order of the source ($m$), which can spatially vary in wall-bounded turbulent flow. Here, we present an adaptive tuning and discuss the influence of $m$ across different TBLs. We introduce the concept of causally coherent structures (CCS), i.e. coherent structures interpreted as spatio-temporal patterns of causality. Moreover, the net transfer entropy flux is also utilised to identify boundary layer locations acting either as sources or targets. The standard viscous, logarithmic, and outer layers are characterised by information fluxes, highlighting, for example, dominant top-down interactions between the inner and outer layers, analogously to the classical energy cascade. This work extends techniques previously employed in the literature, such as correlation and spectral analysis, and presents an approach that is inherently general and applicable to a wide range of chaotic dynamical systems, with applications in cognitive sciences, systems biology and finance.

One-dimensional chains of coupled spins are minimal models of strongly correlated quantum matter, and have been proposed as wires for transporting quantum information. In liquids, rapid molecular tumbling averages anisotropic dipolar couplings and leaves effective isotropic scalar $J$-coupling Hamiltonians. At zero- to ultralow-field (ZULF) conditions, differences in frequency between nuclear spins of different types are quenched and the internal Hamiltonians can be closely approximated by an isotropic Heisenberg model. In this work, we present [U-$^{13}$C,$^{15}$N]-butyronitrile as a chemically engineered nuclear spin chain whose full spin-spin coupling network can be determined and validated by combining high-field NMR detection with evolution at ultralow fields. Starting from high-field (16.4 T) NMR spectra of $^1$H, $^{13}$C, and $^{15}$N nuclei, we extract all relevant $J$-couplings within a 12-spin network (four $^{13}$C, one $^{15}$N, and seven $^1$H). We then employ a mechanical field-cycling apparatus to prepolarize the spins at high field, shuttle them into a magnetically shielded region for evolution at <50 nT, and detect signals after returning to high field. Fourier analysis of the ultralow-field evolution yields indirect $J$-spectra that are conceptually analogous to ZULF NMR spectra but measured by a high-field NMR spectrometer. We observe clear spectral features at $J$, 1.5$J$, and 2$J$, in good agreement with simulations using the extracted coupling matrix. Finally, we demonstrate 2D experiments that correlate high-field chemical shifts and, thus, fully map interactions within the molecular spin chain. Our results establish [U-$^{13}$C,$^{15}$N]-butyronitrile as an extremely well-characterized spin chain model system and provide a quantitative Hamiltonian benchmark for future hyperpolarization and quantum-control studies.

The design of high-performance optical systems remains a specialized domain gated by the limited availability of expert engineers, creating a bottleneck that stalls innovation despite the growing demand for imaging hardware. While deep learning has improved parameter optimization, it has yet to address the fundamental challenge of conceptualizing valid optical architectures from functional requirements. Here, we present an end-to-end generative framework that couples the semantic reasoning of Large Language Models (LLMs) with a differentiable ray-tracing engine to democratize the synthesis of diffraction-limited optical prescriptions. By treating optical design as a semantic-to-physical translation task, the system autonomously interprets prompts ranging from high-level end-user requests to rigorous technical specifications. We demonstrate the framework's versatility across three distinct regimes: (1) finite-conjugate industrial metrology systems, where the model autonomously enforces application-specific constraints such as telecentricity to achieve diffraction-limited performance; (2) a suite of infrared objectives (NIR, SWIR, and LWIR), demonstrating the framework's ability to synthesize valid topologies and optical prescriptions for non-visible spectral bands, and (3) complex aspheric mobile lenses, where the system successfully navigates the high-dimensional optimization landscape to produce high-resolution designs suitable for modern sensors. Validated against industry-standard simulation tools, these results establish a new paradigm for automated optical engineering, bridging the gap between semantic intent and physical realization.

A framework for reconstructing the one-electron spinors, $\Gamma_7$ and $\Gamma_8$, of \ch{Cr^3+} ions embedded in glasses from optical measurements has been developed. These spinors provide the basis for calculating the spin-orbital von Neumann entropy, offering a quantitative measure of quantum entanglement within the electronic state. To illustrate the applicability of this concept, an aluminum phosphate glass doped with 1 mol$\%$ chromium was prepared and characterized via optical absorption spectroscopy. By extracting the fundamental electronic parameters, including the spin-orbit coupling constant $\xi_{\rm 3d}$, the crystal field strength $Dq$, and the Racah parameters $B$ and $C$, we demonstrate how the spin-orbital entanglement entropy, $\Delta S_{\rm vN}^{\rm SO}$, can be mapped across different chemical environments. Our analysis reveals that while individual crystal field parameters do not dictate the degree of entanglement, the dimensionless ratio between the spin-orbit coupling and the crystal field strength ($\xi_{\rm 3d}/Dq$) exhibits a robust linear correlation with the entropy. This relationship serves as a clear illustration of how the competition between relativistic effects and local symmetry governs the information content of the 3d($O_h$) electronic manifold.

Highly charged ions (HCIs) combine compact electronic structure with strong relativistic effects, offering both robustness against external perturbations and enhanced sensitivity to variations of the fine-structure constant. Recent advances in sympathetic cooling and trapping enable precision measurements of highly charged ions; however, fully exploiting their potential requires accurate theoretical predictions. In particular, reliable calculations of clock wavelengths are essential for experimentally locating HCI clock transitions. Here, we treat Cf17+ as a univalent ion and perform calculations within the relativistic coupled-cluster framework, iteratively including nonlinear single-double contributions and valence and core triple excitations. We also assess quantum-electrodynamic corrections and basis-set and partial-wave truncation effects. Our results establish the impact of different correlation contributions on the low-lying energy spectrum and provide a quantitatively reliable prediction of the 5f_5/2 - 6p_1/2 clock transition, highlighting the critical role of core-valence correlations and iterative triples for precision spectroscopy and optical clock development.

Synthetic survey data generated by large language models (LLMs) suffers from a fundamental circularity: the same model family that generates text responses also maps them to numerical scales. We calibrate and validate Semantic Similarity Rating (SSR; Maier et al., 2024), which decouples generation from scale mapping via embedding-based cosine similarity against predefined anchor statements. Configuration experiments (N=17 pilot, N=69 cross-validation across 8 domains) show that naturalistic behavioral anchors outperform formal jargon by 29 percentage points (pp), and that SSR achieves 65-67% exact match and 91% within plus/minus 1; a cross-model test with OpenAI text-embedding-3-small reaches 77% exact, confirming cross-provider generalization. Direct LLM baselines (Claude 87%, GPT-4o 83%) establish that SSR's contribution is methodological independence, not accuracy superiority. A control condition removing question text from the LLM prompt actually improves LLM accuracy, ruling out information asymmetry as the explanation for SSR's lower accuracy. A pre-registered circularity experiment (N=345) reveals 4x compressed error variance in LLM rating (sigma^2 = 0.21 vs 0.87 for SSR) and systematic directional bias. A cross-model control (GPT-4o rating Claude-generated text) shows nearly identical compression (within/cross ratio = 0.93), indicating variance compression is a general LLM property rather than a within-model artifact. The calibration dataset, anchor library, and source code are publicly available (see Data Availability).

A central challenge across science and engineering is to build data-driven reduced-order models of turbulent dynamical systems that reproduce stationary statistics, predict responses to external perturbations, and remain practical for real-world applications. To this end, we introduce an abstract discrete-time formulation of turbulent dynamical systems with exact energy-conserving nonlinearities. Parameterizing this structure with neural networks yields stable, physics-constrained reduced-order models. We then use the fluctuation-dissipation theorem (FDT) to validate the emulators' forced responses from unperturbed data alone, testing models beyond stationary statistics. The FDT also identifies candidate direct causal links, which are used as regularization terms only when they pass validation. We first test the framework on two idealized models of geophysical turbulence: the proposed emulators reproduce stationary statistics and accurately predict responses to weak and strong forcings, despite being trained solely on unperturbed data. We then move beyond idealized systems to model tropical climate dynamics from reanalysis data, where data scarcity, partial observability, and sensitivity to choices of stochastic parameterizations become central challenges. The resulting physics-constrained model reproduces key statistics of the El Niño-Southern Oscillation (ENSO) and qualitatively captures its cumulative responses to perturbations; augmenting it with a non-Markovian stochastic closure substantially improves quantitative agreement with the FDT benchmark. This response-validated model is then used to characterize long-term causal drivers of ENSO variability. The proposed methodology establishes a modular framework for stable reduced-order models capable of probing causal mechanisms in realistic, partially observed turbulent systems.

We present an anisotropic goal-oriented error estimator based on the Dual Weighted Residual (DWR) method for time-dependent convection-dominated problems. Using elementwise p-anisotropic finite element spaces, the estimator is elementwise separated with respect to the single directions in space and time. This naturally leads to adaptive, anisotropic hp-refinement (h-anisotropic refinement and elementwise anisotropic p-enrichment). We employ discontinuous elements in space and time, which are well suited for problems with high Peclet numbers. Efficiency and robustness of the underlying algorithm are demonstrated for different goal functionals. The directional error indicators quantify anisotropy of the solution with respect to the goal, and produce hp-refinements that efficiently capture sharp layers. Numerical examples in up to three spatial dimensions demonstrate the superior performance of the proposed method compared to isotropic h and hp adaptive refinement using established benchmarks for convection-dominated transport.

In this work, we report implementation and performance evaluation of memristor-driven fundamental logic gates, including NOT, AND, NAND, OR, NOR, and XOR, and novel and optimized design of the sequential logic circuits, such as D flip-flop, T-flip-flop, JK-flip-flop, and SR-flip-flop. The design, implementation, and optimization of these logic circuits were performed in SPECTRE in Cadence Virtuoso and integrated with 90 nm CMOS technology node. Additionally, we discuss an optimized design of memristor-driven logic gates and sequential logic circuits, and draw a comparative analysis with the other reported state-of-the-art work on sequential circuits. Moreover, the utilized memristor framework was experimentally pre-validated with the experimental data of Y2O3-based memristive devices, which shows significantly low values of variability during switching in both device-to-device (D2D) and cycle-to-cycle (C2C) operation. The performance metrics were calculated in terms of area, power, and delay of these sequential circuits and were found to be reduced by more than ~24%, 60%, and 58%, respectively, as compared to the other state-of-the-art work on sequential circuits. Therefore, the implemented memristor-based design significantly improves the performance of various logic designs, which makes it more area and power-efficient and shows the potential of memristor in designing various low-power, low-cost, ultrafast, and compact circuits.

The spinel phase (wadsleyite, ringwoodite) in the mantle transition zone (MTZ), can contain up to 1-2 weight percent of water. However, whether these water reservoirs in the MTZ are filled is debated. Here, we investigate water dynamics in the MTZ numerically by using a newly developed empirical model of deep hydrous mantle melting combined with 2D thermo-hydro-mechanical-chemical (THMC) upper mantle models. Numerical modeling results suggest that water-induced buoyancy triggers the development of hydrous solid-state mantle upwellings in the MTZ. On time scales of some tens of millions of years, they rise to and interact with the spinel-olivine phase transition. Depending on the water content and temperature of these thermal-chemical plumes, this crossing may trigger hydrous melting by water release from the wadsleyite upon its conversion to olivine. The melts are less dense than the solid matrix and continue rising upward in the form of either diapirs or porosity waives. Similar dehydration-induced melting process3 is also documented for the lower MTZ boundary, where hydrous downwellings (such as subducted slabs) generate buoyant melt diapirs rising through the MTZ. We therefore suggest that the MTZ operates as a transient water reservoir. Relatively small amounts of water (less than 0.1 weight percent, smaller than 0.2 ocean masses) and a geologically moderate duration (80-430 Myr) of the transient water storage should be characteristic for the MTZ, which may play a key role in stabilizing the surface ocean mass on Earth and Earth-like rocky exoplanets.

Physics-Informed Neural Networks present a novel approach in SciML that integrates physical laws in the form of partial differential equations directly into the NN through soft constraints in the loss function. This work studies the application of PINNs to solve a one dimensional coupled electro-elastodynamic system modeling linear piezoelectricity in stress-charge form, governed by elastodynamic and electrodynamic equations. Our simulation employs a feedforward architecture, mapping space-time coordinates to mechanical displacement and electric potential. Our PINN model achieved global relative L2 errors of 2.34 and 4.87 percent for displacement and electric potential respectively. The results validate PINNs as effective mesh free solvers for coupled time-dependent PDE systems, though challenges remain regarding error accumulation and stiffness in coupled eigenvalue systems.

Untrained neural networks (UNNs) offer high-fidelity electromagnetic inverse scattering reconstruction but are computationally limited by high-dimensional spatial-domain optimization. We propose a Real-Time Physics-Driven Fourier-Spectral (PDF) solver that achieves sub-second reconstruction through spectral-domain dimensionality reduction. By expanding induced currents using a truncated Fourier basis, the optimization is confined to a compact low-frequency parameter space supported by scattering measurements. The solver integrates a contraction integral equation (CIE) to mitigate high-contrast nonlinearity and a contrast-compensated operator (CCO) to correct spectral-induced attenuation. Furthermore, a bridge-suppressing loss is formulated to enhance boundary sharpness between adjacent scatterers. Numerical and experimental results demonstrate a 100-fold speedup over state-of-the-art UNNs with robust performance under noise and antenna uncertainties, enabling real-time microwave imaging applications.

The voltage power spectral density measured around the ambient plasma frequency in space is not affected by spacecraft perturbations that impact traditional plasma analysers. The spectroscopy of this noise, produced by the quasi-thermal motion of ambient charged particles, is thus an efficient tool for measuring in situ plasma properties in space. In contrast, the spectrum at lower frequencies, which is determined by the parallel antenna resistance due to electric currents, depends on the spacecraft local environment. Recently, \citet{zhe26} erroneously estimated this resistance from Parker Solar Probe (PSP) data. We hereby present a theoretical calculation of this resistance, which determines the shot noise and the receiver gain at low frequencies, and provide a preliminary comparison to PSP/FIELDS data. We also show that this resistance can change the receiver gain in the frequency range used for QTN spectroscopy during PSP inner orbits.