Optics
Feature-domain Fourier ptychographic tomography with dark-field illumination
Chao Tan, Fangrui Lu, Sechan Park et al. (+6 more)
Fourier ptychographic
tomography (FPT) is an implementation of intensity diffraction
tomography that reconstructs three-dimensional (3D) refractive-index (RI) distributions from angle-varied intensity measurements. The distinctive advantage of FPT emerges when incorporating dark-field illumination, which extends the space-bandwidth product toward gigavoxel-scale volumetric imaging, yet dark-field measurements are highly sensitive to system imperfections and often have low signal-to-noise ratios. Here, we propose feature-domain FPT (FD-FPT), which evaluates data fidelity after feature extraction and is optimized using automatic differentiation. In numerical and experimental tests, FD-FPT resolves structures near the synthetic-aperture cutoff far more reliably than the spatial-domain baseline (SD-FPT). Notably, in a whole-mount Oedogonium specimen, the reticulate chloroplast network and transverse septa were resolved only by FD-FPT. We further demonstrate a 1.81-gigavoxel RI reconstruction of a mouse adrenal gland section across a 1.66 by 1.40 square millimeter field of view, establishing FD-FPT as a practical route to label-free volumetric imaging that combines millimeter-scale coverage with cellular-scale structural contrast.
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All authors Chao Tan, Fangrui Lu, Sechan Park, Hyeonseo Na, Chanseok Lee, Chang-Seok Kim, Jeesu Kim, Hwidon Lee, Mooseok Jang
Geophysics
Simple data fusion from several ocean and atmosphere hindcast models improves surface drifter trajectory prediction
Jean Rabault, Knut Frode Dagestad and Gaute Hope
Simulating the trajectory of surface drifters in the ocean matters for search and rescue, pollution tracking, oil and chemical spill response, and marine risk analysis. Accurate prediction remains difficult, as widely acknowledged in the literature, and also illustrated by the “
Forecasting Floats in
Turbulence” challenge issued by the US Defense Advanced Research Projects Agency (DARPA) in 2021, and which ultimately led to this paper. The main source of error usually comes from uncertain ocean currents, while errors in wind forcing and object drift properties are often smaller [Dagestad and Röhrs, 2019]. Here, we use an open one-year
dataset of Sofar Spotter trajectories together with several ocean and atmospheric hindcast products to test data-driven drift models at scale. We compare three approaches: i) a standard (baseline) drifter trajectory simulation based on one ocean model and one atmospheric model, ii) linear regression (LR) models that fuse all available predictors, and iii) neural networks (NN) using similar inputs. A simple LR model that combines all predictors performs equally well as the NN. Because LR is simpler, cheaper, and more robust, we retain it as the preferred approach. In 2-day trajectory prediction, this improves the Liu-Weisberg skill score by around 40% relative to the baseline. These findings apply to hindcast mode; applying this methodology for forecast mode remains for future work.
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Medical Physics
A scattering correction method for CT reconstruction based on the Wavelet Adaptive Material-dependent Boltzmann Transport Equation (WAM-BTE)
Huiying Pan, Xu Jiang and Xing Zhao
X-ray computed
tomography (CT) is an essential imaging technology in clinical diagnosis. However, scattered
photons can reduce image contrast and introduce CT value bias, which severely degrades image quality. Recently, scatter correction methods based on the Boltzmann transport equation (BTE) have attracted increasing
attention due to their high physical accuracy and flexibility. Nevertheless, existing BTE-based methods usually employ single-material models, which cannot accurately describe the nonlinear energy dependence of
photon interaction cross-sections in different materials. In this work, a scatter correction method based on the wavelet adaptive material-dependent BTE (WAM-BTE) is proposed. The conventional single-material model is extended to a multi-material model by introducing material-dependent scattering distributions. Furthermore, an adaptive multi-scale framework is established through wavelet decomposition. The low-frequency wavelet coefficients are used for coarse-scale scatter estimation to reduce computational complexity, while the high-frequency wavelet energy of high attenuation materials is utilized for adaptive local refinement. Theoretical analysis demonstrates that, when using the Haar basis function, the low-frequency wavelet coefficients at the $w$-th level are mathematically equivalent to block-average downsampling with a scale factor of $2^w$, except for a deterministic normalization factor. Experimental results show that the proposed WAM-BTE method achieves comparable accuracy to the Monte Carlo method while preserving the computational efficiency of coarse-scale estimation. The scatter calculation time for a single projection view is reduced to the millisecond level.
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Applied Physics
On the Fundamental Limits of Single-snapshot Compressive Ultrasound Imaging Using Random Aberrative Masks
Zehua Dou, Yaokuan Zhang, Jialong Zhang et al. (+3 more)
Compressive sensing emerges as a paradigm shift to realize real-time volumetric ultrasound imaging using a single element transducer equipped with an aberrative mask. Such a coded aperture encodes each scatterer in the FoV as a specific echo signal, compressing each volumetric into a time sequence that are reconstructed into image via computational methods. Compressive imaging (CI) can greatly reduce the data rate and simplify the electronics. Despite this potential, the practical performance of single-snapshot CI remains unclear, especially how it is influenced by mask design, imaging task complexity, and reconstruction strategy. For this, we separate the information budget provided by the mask from the
algorithm-dependent extraction of the encoded information. First, the spatial impulse responses of random masks with different pixel sizes and time-delay ranges were calibrated, and the
entropy-based effective rank of their similarity
matrix was used to quantify the available encoding capacity. Masks with pixel size of approx. half wavelength and a time-delay range of two carrier periods provided higher encoding capacities, reaching up to 1.1% of the total sampled voxels.
Algorithm-dependent information extraction was further evaluated for both ULM-motivated particle localization and B-mode imaging. For particle localization, L1-norm regularized least-squares method reconstructed particles corresponding to approx. 10% of the available encoding capacity, outperforming the matched filter. A transition from successful to failed reconstruction was observed when the number of particles exceeded this value, revealing the upper limit of the present CI systems using random masks. For B-mode imaging, LSQR achieved the highest SSIM among the evaluated methods, of up to 0.12, although the encoding capacity of the current random masks remained insufficient for high-fidelity reconstruction.
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All authors Zehua Dou, Yaokuan Zhang, Jialong Zhang, Cherif Othmani, Lars Büttner, Jürgen W. Czarske
Atmospheric and Oceanic Physics
Video Diffusion for Satellite-based High-Dynamical-Fidelity Precipitation (HiDFiP) Field Generation
Runze Li, Yan Xia, Yongquan Qu et al. (+6 more)
High spatiotemporal fidelity precipitation products that accurately capture storm spatial organization, propagation, and lifecycle
evolution, are essential for advancing hydrometeorological research and operations at regional and global scales. Satellite products offer the only near-global precipitation observations, but they still fall short of reproducing the spatiotemporal structure of ground-based references, due largely to dynamic distortions from the inhomogeneity, intermittency, and indirectness of satellite
retrievals. Here we propose a
video-
diffusion framework for satellite-based High-Dynamical-Fidelity Precipitation (HiDFiP) field generation beyond the space-time coverage of ground-radar, using radar-rich CONUS as a testbed. The framework performs explicit spatiotemporal modeling with IMERG as the primary source and leverages four-dimensional storm-environment information from about 40 ERA5/ERA5-Land atmospheric/land fields to compensate for the temporal information deficit inherent to satellite
retrievals. We introduce an extensive metric suite to assess HiDFiP dynamical fidelity in temporal-reconstruction and spatial-transfer settings against MRMS ground-radar precipitation over CONUS. Relative to IMERG and image-wise
diffusion baselines, HiDFiP accurately reproduces the storm space-time spectral characteristics; storm timing, location, and directional propagation; precipitation-event episodicity and temporal structure; precipitation-system morphology and spatial organization; and storm-track kinematics and lifecycle
evolution. Transferability experiments indicate that HiDFiP generalizes reasonably well to an unseen region. This work advances a
video-
diffusion paradigm for satellite-based high-spatiotemporal-fidelity precipitation generation and provides an algorithmic and diagnostic foundation for long-term global radar-grade precipitation records.
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All authors Runze Li, Yan Xia, Yongquan Qu, Dongwei Fu, Clement Guilloteau, Judy Hoffman, Stephan Mandt, Pierre Gentine, Efi Foufoula-Georgiou
Instrumentation and Detectors
An integrated micro-thermometer for mid-infrared photothermal spectroscopy of dense materials built in a CMOS 200 mm pilot line
Sandy Mathew, Sonia Messaoudène, Adrien Poizat et al. (+6 more)
Indirect photothermal infrared spectroscopy of dense materials typically relies on photoacoustic
cells for detecting acoustic waves generated by light-induced heating. This detection approach can be challenging to implement as it is highly sensitive to the sealing quality of the interface between the cavity and the sample, as well as to the properties of the gas within the cavity. Here, we present a simplified and more robust method. This approach relies on direct photothermal temperature measurement using an optimized platinum microsensor fabricated on a thin silicon substrate that acts as a thermally transparent interface with the sample. The sensor design, fabrication in a 200 mm CMOS pilot line, assembly on a readout PCB, and experimental characterization are reported. The component achieves a signal-to-noise ratio close to 1000 making it well-suited for miniaturized and embedded infrared spectroscopic applications.
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All authors Sandy Mathew, Sonia Messaoudène, Adrien Poizat, Éléa Bourliaud, Marion Volpert, Jules Skubich, Stanislas Lhomme, Bertrand Bourlon, Kevin Jourde
Optics
Critical Topological Photonics in Synthetic Dimensions
Mingyuan Chen, Yuwei Jing, Franco Nori et al. (+1 more)
Topological states and criticality have long been regarded as incompatible ingredients: the former requires a finite spectral gap, whereas the latter demands its closure. Guided by this view, topological photonics has focused almost exclusively on gapped phases, treating gap-closing transitions as mere phase boundaries. In this work, we propose a class of topological states in which
topology coexists with criticality in experimentally accessible synthetic-frequency photonic platforms. In a one-dimensional (1D) synthetic
lattice, we identify such critical topological photonic states through midgap degeneracies in the single-particle
entanglement spectrum, and uncover a
topology-enforced multicritical point that reorganizes the
topology of neighboring critical states. We further extend this framework to two dimensions (2D). Our work provides an experimentally accessible route to critical topological photonics, and may also inspire novel applications such as critical topological sensing.
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All authors Mingyuan Chen, Yuwei Jing, Franco Nori, Xue-Jia Yu
Optics
Coexistence of Magnon-Induced Optical Vortex and Gaussian Beam Scattering Assisted by Rotational Umklapp Process
Ryusuke Hisatomi, Kotaro Taga, Hisakazu Matsuki et al. (+3 more)
The exploitation of
crystal-
lattice symmetries to engineer angular momentum transfer between structured light and
magnons marks a novel frontier for optomagnonic research. In Brillouin light scattering, when focused light propagates parallel to an external
magnetic field and interacts with
ferromagnetic uniform
magnons, only optical-
vortex scattering is expected to be permitted. Due to the combined effects of magneto-optical coupling and optical
spin-orbit interaction, the transfer of
magnon spin angular momentum to
photon orbital angular momentum allows for this distinctive scattering phenomenon. Here, we experimentally demonstrate that, for a specific
ferromagnetic crystal orientation, Gaussian-beam scattering coexists with the optical-
vortex scattering, contrary to conventional expectations based on angular momentum conservation between
magnons and
photons. We show that the
crystal lattice, via the rotational Umklapp process, provides the missing angular momentum required for the Gaussian-beam scattering. Furthermore, we predict that as the degree of light focusing increases, the relative efficiencies of the Gaussian-beam and optical-
vortex scattering processes reverse.
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All authors Ryusuke Hisatomi, Kotaro Taga, Hisakazu Matsuki, Shutaro Karube, Yoichi Shiota, Teruo Ono
Fluid Dynamics
On the degradation of hot spot performance due to mid-to-high-mode hydrodynamic instabilities
Dongxue Liu, Jiaqin Dong, Yunxing Liu et al. (+5 more)
In an ignited design of inertial confinement fusion, the role of mid-to-high-mode hydrodynamic instabilities in degrading hot-spot performance, beyond reducing temperature, remains unclear. To address this, we propose an isobaric criterion to assess the isobaric assumption that forms the theoretical basis of the hot spot. The most dangerous mode l = 12 is determined through a balance between perturbation growth and ablation stabilization induced by thermal conduction. Thermal conduction outperforms convection when the Peclet number is much less than 1. Therefore, for mid-to-high modes, thermal conduction makes the hot spot isobaric before the outer mass inflow restores the lost heat. Consequently, neglecting thermal conduction overestimates pressure and underestimates volume. These results enhance our understanding of mid-to-high modes in degrading hot-spot performance, and suggest that thermal conduction losses may reduce performance even if perturbations are nearly stabilized by ablation.
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All authors Dongxue Liu, Jiaqin Dong, Yunxing Liu, Zhiyu He, Wei Wang Jinren Sun, Yuqiu Gu, Xiuguang Huang, Jian Zheng
Optics · Cross-listed from quant-ph
Reinventing the Single-pixel Imaging Paradigm via Quantum-Operator-Based Signal Processing
Bu-Ran Yu, Yi-Zhu Zhang, Kan-Xu Jia et al. (+3 more)
A fundamental bottleneck across modern computational imaging and high-dimensional sensing is the conventional decoupled acquisition-reconstruction hierarchy, which subjects high-dimensional spatial sensing to the classical shot-noise limit and intense computational overhead. As a prominent manifestation of this limitation, single-pixel imaging (SPI) suffers severely from this paradigm. We reinvent this paradigm by introducing a
quantum-operator-based SPI theoretical framework driven by coherent signal processing. Within this architecture, the spatial inverse problem is analytically mapped into the
eigenvalue spectrum of a
quantum operator via tailored light-matter interactions. By analytically synthesizing non-linear reconstruction operators via ultra-shallow
quantum architectures, we theoretically demonstrate an exponential decay of spatial approximation errors, completely bypassing traditional linear solvers. This operator-space
embedding not only shields reconstruction from noise via a strategic error-saturation zone but also bridges the gap from classical shot-noise scaling $\mathcal{O}(1/\sqrt{N_{\text{ph}}})$ to the ultimate Heisenberg limit $\mathcal{O}(1/N_{\text{ph}})$. Crucially, while formulated within SPI, this coherent operator paradigm fundamentally extends to general
photon-starved, high-dimensional imaging modalities. This work establishes a universal theoretical blueprint for next-generation
quantum-enhanced sensing, shifting the paradigm from iterative optimization to coherent operator-space
evolution.
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All authors Bu-Ran Yu, Yi-Zhu Zhang, Kan-Xu Jia, Qian-Qian Bao, Shao-Ying Meng, Xi-Hao Chen
Geophysics · Cross-listed from cs.DC
Rapid Earthquake-to-Tsunami Waveform Generation via Large-Scale Multi-GPU FFT Convolution Applied to the Cascadia Subduction Zone
Bowen Shi, Sreeram Venkat, Stefan Henneking et al. (+1 more)
Data-driven methods for earthquake and tsunami early warning rely on large ensembles of rupture scenarios and their resulting waveforms, but generating such
datasets with repeated high-fidelity seismic and tsunami simulations is prohibitively expensive. We exploit the linear time-invariant structure of both dynamics to precompute elastic Green's functions and acoustic-
gravity adjoint responses, reducing the source-to-waveform map to two consecutive convolution operators. We evaluate these convolutions with a distributed, FFT-accelerated GPU pipeline that partitions the large seafloor grid across GPUs and directly generates the final observation waveforms. We demonstrate the scalability of this pipeline for the Cascadia Subduction Zone with 963 subfaults, 2,416,530 seafloor grid points, 64 observation locations, and 256 timesteps, requiring 9.45 TiB of aggregate GPU
memory. On 64 GB200 GPUs within one NVL72 domain, the pipeline generates waveforms in 24 ms per rupture once the response operators are resident, enabling large rupture ensembles to be evaluated within minutes.
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All authors Bowen Shi, Sreeram Venkat, Stefan Henneking, Omar Ghattas
Instrumentation and Detectors · Cross-listed from cs.LG
How Architecture and Training Affect TPC Representations Across Experiments
Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan et al. (+12 more)
Deep-learning efforts have increasingly shifted toward foundation model approaches. In experimental physics, this allows models and learned representations to be reused beyond the experiments in which they were developed. This work evaluates the reusability of representations across experiments and detector systems using probes on frozen encoders. These probes reveal task-relevant structure before downstream adaptation, complementing fine-tuning. Together with random-weight controls, they distinguish contributions from architecture and encoder training that downstream performance alone cannot resolve. Time projection chamber (TPC) data provide a useful testbed because events from TPC systems can be represented as variable-length sparse tensors, while detector geometries, event
topologies, and scientific tasks can differ substantially. We investigate whether fixed-dimensional TPC event representations can be reused across classification tasks, experiments, and detector systems. Sparse ResNet and PointNet-style encoders produce 512-dimensional
embeddings for four
datasets from the GADGET II TPC and AT-TPC. Randomly initialized encoders isolate the contribution from architecture before supervised training. We then train each encoder on a classification task, freeze its parameters, and train a linear or nonlinear probe for each downstream task. We find that this architecture-induced structure remains useful across experiments and detector systems. The randomly initialized PointNet-style representation is highly informative on several tasks. The two architectures organize their
embedding spaces differently, but neither exhibits a large, systematic loss of utility cross-detector. These results show that architecture is a major source of task-relevant structure in TPC
embeddings and should be treated explicitly when assessing representation learning and developing reusable detector models.
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All authors Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan, William Sieland, Ryan Krupp, Yassid Ayyad, Daniel Bazin, Connor L. Cross, Hoi Yan Ian Heung, Andrew J. Jones, Ruchi Mahajan, Saiprasad Ravishankar, Pranjal Singh, Benjamin Votaw, Chris Wrede
Computational Physics · Cross-listed from cond-mat.soft
Pitch-controlled reorientational nonlinearity in chiral nematic liquid crystals: a reduced-order model for self-focusing and soliton formation
Homa Saadatmand, M. Javad Zakeri, Ahmed Sameh Ahmed et al. (+6 more)
We present a reduced-order semi-analytical model for reorientational nonlinearity in
chiral nematic
liquid crystals, showing that the
chiral pitch acts as the dominant physical length scale governing the onset of nonlinear self-focusing and
soliton formation. Starting from the full Frank-Oseen equation, we derive a closed-form expression for the optically induced molecular rotation that captures the essential saturable response of the medium while reducing computational cost by more than two orders of magnitude compared with standard relaxation-method solvers. Despite its simplicity, the model reproduces the essential features of the numerically obtained nonlinear refractive index, the onset of self-localization, and the transition from discrete to continuous
solitons in one and two dimensions. It further predicts the formation of fully localized astigmatic nematicons with only minor shifts in the self-localization threshold due to the neglect of nonlocal effects. The proposed model provides direct physical insight into light-matter interactions with soft matter media and offers a computationally efficient tool for the design and optimization of nonlinear photonic devices.
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All authors Homa Saadatmand, M. Javad Zakeri, Ahmed Sameh Ahmed, Suraj Bhandari, Loubna Benkoula, Ameer B. Batarseh, Andrea Blanco-Redondo, Miroslaw Karpierz, Pawel S. Jung
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Thermodynamic and Kinetic Tests for Low-Pressure Metal-Flux Growth of Diamond, with an Extension to Cubic Boron Nitride
Rodney S. Ruoff
Diamond was grown from a
liquid Ga-Fe-Ni-Si
alloy near 1 atm, although bulk graphite is thermodynamically more stable under those conditions. This Perspective asks what must be true for a diamond growth front to continue advancing in such a metastable regime. I define a finite-difference transfer free energy for a specified interfacial event and identify the completed attachment of one carbon atom at a kink as the repeatable event that governs sustained growth. In the macroscopic limit, surface terms cancel, and the required carbon activity is fixed by the bulk free energy of diamond. On a graphite-normalized scale, the diamond
equilibrium threshold is about 2.0 at 1300 K, whereas graphite saturation occurs at unity. Diamond growth therefore requires kinetic selection above the graphite threshold, not a reversal of bulk phase stability. The analysis distinguishes thermodynamic driving force, kinetic accessibility, experimental detection limits, and the carbon retained after an experiment. It gives isotope-resolved tests for distinguishing sustained diamond growth from seed survival, transient carbon attachment, or failure to detect graphite. A $^{13}\mathrm{C}$-enriched diamond seed provides a test of natural-abundance gas or condensed carbon sources: a registered, crystallographically continuous $^{12}\mathrm{C}$-rich layer outside the original seed can establish source-attributed overgrowth without isotopically labeling each feed. The same accounting is extended to cubic boron nitride. Separate boron and nitrogen chemical potentials are required, and $^{10}\mathrm{B}/^{11}\mathrm{B}$ and $^{15}\mathrm{N}$ contrasts could distinguish reservoir supply from seed loss for each element. The resulting criteria make low-pressure metal-flux growth claims quantitatively testable and guide experiments, thermodynamic modeling, and atomistic simulation.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci
First-Principles Atomistic Structure and Dynamics of Polyethylene During High-Pressure Radical Polymerization via Machine Learning Force Fields
Bharatha K. Gunawardana, Teresa Shah, Bicha Azizova et al. (+6 more)
Polyethylene (PE) is one of the most commonly used synthetic
polymers. While the synthesis and processing protocols for PE are well established, precise experimental assignment of microscopic structures at atomistic resolution (i.e., the position of each atom) remains largely limited to highly crystalline systems. This gap is often addressed via computer simulations using empirical interatomic potentials, which use approximate but efficient descriptions of interatomic interactions to reach the length and time scales needed to describe macromolecules. These empirical potentials typically perform well for bulk and/or collective properties but face challenges with chemical realism for complex systems, e.g., during reactive processes. In this work, we address this challenge by combining the computational efficiency of a deep potential (DP) machine-learning force field and the chemical realism of first-principles van der Waals (vdW) corrected hybrid density functional theory (DFT) enabled by a SeA high-throughput framework. Using this approach, we study the structure and dynamics of PE oligomers and
polymers in an ethylene solvent under common high-pressure (supercritical) radical polymerization conditions. We found that the local solvation environment of radical-containing PE oligomers converges for chain lengths greater than (n~6), suggesting extensibility of our oligomer-trained MLFF to significantly longer
polymers. We then confirmed the extensibility of these models to long PE chains by characterizing the molecular weight scaling of single-chain structure and dynamics, which showed classic good solvent behavior. Our PE MLFF retained a consistent level of fidelity and stability across a wide range of thermodynamic state points and chain lengths, at full atomistic resolution, therefore paving the way towards first-principles-based
polymer structure and property prediction.
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All authors Bharatha K. Gunawardana, Teresa Shah, Bicha Azizova, Deepa Ranabhat, Yizhi Song, Akshath Shastri, Srinjoy Ghose, Thomas E. Gartner III, Hsin-Yu Ko
Plasma Physics
State-Space Model-Enabled Reinforcement Learning for Magnetic Configuration Controlon EXL-50U
Pei Guo, Zhengyuan Chen, Jianguo Chen et al. (+17 more)
Accurate feedback control of the
plasma current ($I_p$) and centroid position $(R_c,Z_c)$ is essential for the stable operation of spherical
torus (ST)
plasmas. Conventional proportional-integral-derivative (PID) controllers require extensive manual tuning and struggle with the fast, strongly coupled dynamics that arise as
plasma performance improves. Reinforcement learning (RL) has recently emerged as a promising alternative to such complex
magnetic control problems, yet its practical deployment on ST devices remains challenging. This paper presents a practical RL controller for the EXL-50U ST, trained within a rigid RZIP state-space model (SSM) that enables efficient offline policy learning. A lightweight
plasma position reconstructor is developed to estimate $(R_c,Z_c)$ from
magnetic probe signals within the real-time control cycle. The trained policy is seamlessly deployed on the EXL-50U
plasma control system, achieving stable regulation of $I_p$ and $(R_c,Z_c)$ and sustaining discharges up to 650 ms under RL control. These results demonstrate the feasibility and practical potential of model-informed RL for
magnetic configuration control in ST devices, offering a promising direction beyond conventional PID-based schemes.
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All authors Pei Guo, Zhengyuan Chen, Jianguo Chen, Xuanhe Wang, Guoyang Shi, Siqi Ding, Yapeng Zhang, Lei Xing, Yong Liu, Xiang Gu, Tiantian Sun, Xiuchun Lun, Jia Li, Zhengxiong Wang, Huasheng Xie, Hanyue Zhao, Yuejiang Shi, Xianming Song, Tianyuan Liu, EXL-50U Team
Fluid Dynamics
Propagating fronts of convection rolls in Rayleigh-Bénard convection
Saikat Mukherjee and Mark Paul
We investigate the propagation of counter-rotating convection rolls in Rayleigh-Bénard convection initiated locally in a quiescent fluid layer under supercritical conditions. The velocity of the front separating quiescent fluid from the forming convection rolls, and the wavenumber of the convection rolls remaining behind the front, are explored. We numerically investigate fronts of forming convection rolls over five orders of magnitude of the reduced Rayleigh number, $\epsilon$, in 2D and 3D domains, for a broad range of boundary conditions, and for different front initiation approaches. In all cases, the front velocity increases as $\epsilon^{1/2}$ with increasing $\epsilon$ for $\epsilon \lesssim 1$ in agreement with predictions using the amplitude equation. The amplitude equation description of the front velocity remains accurate for $\epsilon \lesssim 10$ except when the Prandtl number is large which yields a velocity that is faster than predicted for a fluid layer far from threshold. The wavenumber of the convection rolls increases linearly with $\epsilon$ in agreement with the wavenumber that maximizes the growth rate of perturbations in the linear regime. Farther from onset, the wavenumber growth transitions to a reduced scaling of $\epsilon^{1/4}$ in agreement with predictions using the Swift-Hohenberg equation in the large $\epsilon$ limit. The scalings describing the wavenumber variation with $\epsilon$ are independent of the domain geometry, boundary conditions, and front initiation method. However, the front-selected wavenumber at criticality does not equal the critical wavenumber of the bulk instability, in general, and depends significantly upon these details. We compare our results with experimental measurements where possible.
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Computational Physics · Cross-listed from math.NA
Wasserstein Moment Nudging for Vlasov-Poisson Data Assimilation
Liyao Lyu, Xinyue Yu, David Schneidinger et al. (+1 more)
We introduce a continuous data assimilation method for particle-in-
cell simulations of the Vlasov-Poisson equation when only hydrodynamic moments are observed. The forecast state is an empirical measure on phase space, whereas the observed fields (density, bulk velocity, and temperature) constrain only a few velocity moments and leave the velocity-space shape of the distribution undetermined. We construct the moment feedback as a Wasserstein gradient flow of a moment-mismatch functional over phase-space measures. The resulting drift acts directly on particle positions and velocities, couples the density, momentum, and energy residuals through a single variational structure, and vanishes on the entire moment-compatible set. Under the standard Wasserstein metric, the energy residual produces a position correction that grows quadratically with the particle speed, and the particle system falls outside standard well-posedness theory. Our primary formulation pairs the quadratic moment mismatch with a velocity-weighted Wasserstein metric that penalizes spatial transport at large peculiar velocity relative to the observed bulk flow, which removes this growth. A direction-split variant retains the plain metric instead. Under the same weighted metric, an alternative moment-relative-
entropy functional yields an affine, shape-preserving velocity correction and explicit global moment balances for the space-inhomogeneous system. We prove that the finite-particle scheme with linear Lenard-Bernstein collisions is globally well posed. In 1D1V and 2D2V experiments with several collision models, the nudged formulations reduce bulk-velocity and temperature errors by up to two orders of magnitude relative to an unassimilated run.
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All authors Liyao Lyu, Xinyue Yu, David Schneidinger, Hayden Schaeffer
Fluid Dynamics
Translation of a spherical viscous drop driven by localized forcing in Stokes flow
Sho Kawakami, Yuan-Nan Young and Howard A. Stone
Localized forcing in the fluid inside or outside a viscous drop can drive drop translation. Using the Lorentz reciprocal
theorem, we derive an integral expression for the translational velocity of a spherical Newtonian drop subject to localized force and source distributions in either fluid and to interfacial traction. For a clean drop, we obtain explicit responses to Stokeslets, force dipoles, rotlets, general second force moments, and source dipoles as functions of position, orientation, and viscosity ratio. Interior forcing obeys a finite selection rule: only force moments through second order and the first source moment contribute directly to translation. Exterior forcing can couple to multipoles of all orders and produces distance-dependent responses. Although different enclosed singularities can produce the same drop velocity, resolving their exterior flows in drop-centered spherical Stokes modes provides additional constraints on the underlying forcing. We also distinguish regularized force distributions, governed by prescribed kernel moments, from resolved rigid particles, governed by low-order surface-traction moments and prescribed slip. The framework unifies these representations and shows how exterior-flow measurements provide information beyond drop translation, laying the foundation for constructing squirmer-like viscous drop solutions with controllable far-field behaviors.
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Biological Physics · Cross-listed from cond-mat.soft
Non-uniform swelling of polyelectrolyte hydrogels: effects of charge regulation
Du Chen, Rudolph Podgornik, David Andelman et al. (+4 more)
We investigate the impact of charge regulation (CR) on the non-uniform swelling behavior of polyelectrolyte hydrogels. The Poisson-Boltzmann theory with electro-elastic coupling between the local
polymer density and elastic deformation is considered. We investigate the spatial distributions of the elastic displacement and
polymer density under different salt concentrations and compare charge-regulated gels with fixed-charge (non-CR) gels of the same net charge. Our results show that the CR induces spatially varying charge fractions, which strengthen the electro-elastic response and lead to stronger non-uniform swelling compared with non-CR gels. These findings provide a theoretical basis for understanding and controlling non-uniform swelling in responsive polyelectrolyte hydrogels.
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All authors Du Chen, Rudolph Podgornik, David Andelman, Xianghong Wang, Linli He, Shigeyuki Komura, Bin Zheng