arXiv subjects

Christopher Miller

Publications and source records attributed to Christopher Miller.

At least 19 recordsLinked to original sources

arXiv2026

Intestinal peristalsis and wrinkling: A novel paradigm

A new computational framework for modeling the intestinal wall as a multi-layered fiber-reinforced continuum is presented. The framework reproduces for the first time physiological motility and overcoming large-displacements limitations (self-contact and volume locking) occurring in classical hyperelastic formulations of soft tissues. We introduce: i) layer-specific functions, segregating active circumferential and longitudinal muscle fibers while maintaining homogeneous passive reinforcement, and ii) a quasi-incompressible volumetric contribution, to handle large peristaltic contractions. Cell electrophysiology is further extended to reproduce both slow waves and spike bursting activities thus mimicking for the first time a localized neural excitation in a three-dimensional geometry of small intestine segment. We introduce a spatio-temporal modulation of contractility to accurately capture activation driven by both slow waves and spike bursts. The overall coupled nonlinear electromechanical boundary valued problem is modeled following the active strain approach. A robust augmented-Lagrangian contact algorithm is also embedded to avoid self-penetration and geometrical instabilities under large displacements. The 8-variables nonlinear governing equations are then discretized using in house P1-P2-P4 finite elements codes implemented within the GetFEM library. Numerical experiments demonstrate the ability of the proposed framework to reproduce physiological peristalsis, i.e., wall contraction greater than 80%, thus allowing full occlusion matching in vivo endoscopic images, and naturally generating wrinkling patterns consistent with experimental observations. We show that an active electromechanics anisotropic heterogeneous modeling strategy is critical for a numerically stable and physiologically accurate representation of gastrointestinal motility.

physics.med-ph↗

arXiv2026

Detecting clear-air turbulence via beam broadening in a Rayleigh-scattering lidar system

The volume of clear-air turbulence (CAT) in the atmosphere at flight cruising altitudes is increasing rapidly, posing a growing problem for civil aviation and resulting in reduced confidence in aviation safety. There are limited remote detection capabilities for CAT, since clear air produces no measurable radar return. Lidar has been proposed as a viable detection methodology, and several systems have been demonstrated. However, these systems have to date demonstrated limited detection ranges of less than 15 km. In this work, we propose a novel lidar-based CAT detection methodology that uses Rayleigh scattering and relies on a differential detector measurement to quantify beam spread and thereby estimate the eddy dissipation rate (EDR), which is the international aircraft-independent metric for quantifying aviation turbulence strength. Additionally, we present experimental results demonstrating the validity of the optical efficiency model used in the detection simulations. We show that, under modest assumptions, a size, weight, and power (SWAP) constrained system that implements this method can detect moderate CAT at ranges in excess of 30 km, equating to two minutes of flight time for typical commercial aviation cruising speeds, which represents a substantial range improvement over prior approaches. This is an important advance because-for the first time-it potentially allows the cabin to be secured before the turbulence is encountered, reducing the injury risk to passengers and flight attendants.

physics.optics↗

arXiv2026

Physics-Grounded Multi-Agent Architecture for Traceable, Risk-Aware Human-AI Decision Support in Manufacturing

High-precision CNC machining of free-form aerospace components requires bounded compensations informed by inspection, simulation, and process knowledge. Off-the-shelf large language model (LLM) assistants can generate text, but they do not reliably execute risk-constrained multi-step numerical workflows or provide auditable provenance for high-stakes decisions. We present multi-agent knowledge analysis (MAKA), a human-in-the-loop decision-support architecture that separates intent routing, tools-only quantitative analysis, knowledge graph retrieval, and critic-based verification that enforces physical plausibility, safety bounds, and provenance completeness before recommendations are surfaced for human approval. MAKA is instantiated on a Ti-6Al-4V rotor blade machining testbed by fusing virtual-machining path-tracking error fields, cutting-force and deflection simulations, and scan-based 3D inspection deviation maps from 16 blades. The analysis decomposes deviation into an evidence-linked pathing component, a drift-based wear proxy capturing systematic evolution across parts, a residual systematic compliance term, and a variability proxy for instability-aware escalation. In a three-level tool-orchestration benchmark (single-step through $\geq$3-step stateful sequences), MAKA improves successful tool execution by up to 87.5 percentage points relative to an unstructured single-model interaction pattern with identical tool access. Digital twin what-if studies show MAKA can coordinate traceable compensation candidates that reduce predicted surface deviation from order $10^{-2}$in to approximately $\pm 10^{-3}$in over most of the blade within the simulation environment, providing a pre-deployment verification signal for risk-aware human decision-making.

cs.MA↗

arXiv2026

Directed Social Regard: Surfacing Targeted Advocacy, Opposition, Aid, Harms, and Victimization in Online Media

The language in online platforms, influence operations, and political rhetoric frequently directs a mix of pro-social sentiment (e.g., advocacy, helpfulness, compassion) and anti-social sentiment (e.g., threats, opposition, blame) at different topics, all in the same message. While many natural language processing (NLP) tools classify or score a text's overall sentiment as positive, neutral, or negative, these tools cannot report that positive and negative sentiments coexist, and they cannot report the target of those sentiments. This paper presents the Directed Social Regard (DSR) approach to multi-dimensional, multi-valence sentiment analysis, comprised of a pair of transformer-based models that (1) detects span-level targets of sentiment in a message and then (2) scores all spans within the message context along three (-1, 1) axes of regard that are motivated by social science theories of moral disengagement and moral framing. We present a data collection and annotation strategy for DSR dataset construction, a transformer-based architecture for span-level scoring, and a validation study with promising results. We apply the validated DSR model on six third-party datasets of online media and report meaningful correlations between DSR outputs and the labels and topics in these pre-existing social science datasets.

cs.CL↗

arXiv2026

HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts

AI weather models now rival leading numerical weather prediction (NWP) systems in medium-range skill. However, almost all still rely on NWP data assimilation (DA) to provide initial conditions, tying them to expensive infrastructure and limiting the practical speed and accuracy gains of ML. More recently, ML-based DA systems have been proposed, which are often trained and evaluated end-to-end with a forecast model, making it difficult to assess the quality of their analysis fields. We introduce HealDA, a global ML-based DA system that maps a short window of satellite and conventional observations directly to a 1{\deg} atmospheric state on the HEALPix grid, using a smaller sensor suite than operational NWP. We treat HealDA strictly as a DA module: its analyses are used to initialize off-the-shelf ML forecast models without any fine-tuning of either. For a variety of off-the-shelf ML forecast models, including FourCastNet3 (FCN3), Aurora, and FengWu, HealDA-initialized forecasts lose less than one day of effective lead time when scored against ERA5. HealDA-initialized FCN3 ensembles similarly trail those of the ECMWF IFS ENS system by < 24 h. We find that forecast error growth in these models is unchanged from HealDA initialization, and the skill gap primarily arises from the larger initial error of the HealDA analysis. Spectral analysis reveals that this stems from overfitting to the large scales and upper-tropospheric fields. We also demonstrate that small changes in the verification setup can shift apparent skill by 12--24h, underscoring the need for consistent scoring. Taken together, these results clarify the current performance of ML-based DA systems and show that a relatively simple, direct observation-to-state network can already provide initial conditions that are usable by state-of-the-art ML forecast models with only modest loss in medium-range skill.

physics.ao-ph↗

arXiv2026

Latent Space Element Method

How can we build surrogate solvers that train on small domains but scale to larger ones without intrusive access to PDE operators? Inspired by the Data-Driven Finite Element Method (DD-FEM) framework for modular data-driven solvers, we propose the Latent Space Element Method (LSEM), an element-based latent surrogate assembly approach in which a learned subdomain ("element") model can be tiled and coupled to form a larger computational domain. Each element is a LaSDI latent ODE surrogate trained from snapshots on a local patch, and neighboring elements are coupled through learned directional interaction terms in latent space, avoiding Schwarz iterations and interface residual evaluations. A smooth window-based blending reconstructs a global field from overlapping element predictions, yielding a scalable assembled latent dynamical system. Experiments on the 1D Burgers and Korteweg-de Vries equations show that LSEM maintains predictive accuracy while scaling to spatial domains larger than those seen in training. LSEM offers an interpretable and extensible route toward foundation-model surrogate solvers built from reusable local models.

math.DS↗

arXiv2025

Latent Space Dynamics Identification for Interface Tracking with Application to Shock-Induced Pore Collapse

Capturing sharp, evolving interfaces remains a central challenge in reduced-order modeling, especially when data is limited and the system exhibits localized nonlinearities or discontinuities. We propose LaSDI-IT (Latent Space Dynamics Identification for Interface Tracking), a data-driven framework that combines low-dimensional latent dynamics learning with explicit interface-aware encoding to enable accurate and efficient modeling of physical systems involving moving material boundaries. At the core of LaSDI-IT is a revised auto-encoder architecture that jointly reconstructs the physical field and an indicator function representing material regions or phases, allowing the model to track complex interface evolution without requiring detailed physical models or mesh adaptation. The latent dynamics are learned through linear regression in the encoded space and generalized across parameter regimes using Gaussian process interpolation with greedy sampling. We demonstrate LaSDI-IT on the problem of shock-induced pore collapse in high explosives, a process characterized by sharp temperature gradients and dynamically deforming pore geometries. The method achieves relative prediction errors below 9% across the parameter space, accurately recovers key quantities of interest such as pore area and hot spot formation, and matches the performance of dense training with only half the data. This latent dynamics prediction was 106 times faster than the conventional high-fidelity simulation, proving its utility for multi-query applications. These results highlight LaSDI-IT as a general, data-efficient framework for modeling discontinuity-rich systems in computational physics, with potential applications in multiphase flows, fracture mechanics, and phase change problems.

physics.comp-ph↗

arXiv2025

A coupled multiscale model of the human cornea accounting for the collagenous microstructure and the extracellular matrix

We present a micro-structurally based finite element model of the human cornea, where we explicitly describe the collagen-crosslink architecture in terms of a trusswork of non-linear struts, and the extracellular proteoglycan matrix in terms of continuum solid elements. We regard the cornea as a composite material with strongly non-linear properties within a finite kinematics framework. This innovative approach is based on two previously developed models, each of which has some drawbacks in describing stromal tissue degeneration. Separation of the continuum phase from the collagen microstructure allows a more realistic capture of the macroscopic phenomena observed in keratoconus pathologies, such as localized deformation.

math-ph↗

arXiv2024

Bottom-Up and Top-Down Analysis of Values, Agendas, and Observations in Corpora and LLMs

Large language models (LLMs) generate diverse, situated, persuasive texts from a plurality of potential perspectives, influenced heavily by their prompts and training data. As part of LLM adoption, we seek to characterize - and ideally, manage - the socio-cultural values that they express, for reasons of safety, accuracy, inclusion, and cultural fidelity. We present a validated approach to automatically (1) extracting heterogeneous latent value propositions from texts, (2) assessing resonance and conflict of values with texts, and (3) combining these operations to characterize the pluralistic value alignment of human-sourced and LLM-sourced textual data.

cs.CL↗

arXiv2024

Two Dimensional Magnetic Current Imaging Via L1-Curl Regularized Divergence Free Wavelet Reconstruction

The reconstruction of current distributions from samples of their induced magnetic field is a challenging problem due to multiple factors. First, the problem of reconstructing general three dimensional current distributions is ill-posed. Second, the current-to-field operator performs a low-pass filter that dampens high-spatial frequency information, so that even in situations where the inversion is formally possible, attempting to employ the formal inverse will result in solutions with unacceptable noise. Most contemporary methods for reconstructing current distributions in two dimensions are based on Fourier techniques and apply a low pass filter to the $B$-field data, which prevents excessive noise amplification during reconstruction at the cost of admitting blurring in the reconstructed solution. In this report, we present a method of current recovery based on penalizing the $L1$ norm of the curl of the current distribution. The utility of this method is based on the observation that in microelectronics settings, the conductivity is piecewise constant. We also reconstruct the current fields using a divergence-free wavelet basis. This has the advantage of automatically enforcing current continuity and halving the number of unknowns that must be solved for. Additionally, the curl operator can be computed exactly and analytically in this wavelet expansion, which simplifies the application of the $L1-\textrm{curl}$ regularizer. We demonstrate improved reconstruction quality relative to Fourier-based techniques on both simulated and laboratory-acquired magnetic field data.

eess.IV↗

arXiv2024

Bounds on Distinguishing Separated Wires Using Magnetic Field Measurements

Magnetic current imaging (MCI) is useful for non-destructive characterization of microelectronics, including both security analysis and failure analysis, because magnetic fields penetrate the materials that comprise these components to enable through-package imaging of chip activity. Of particular interest are new capabilities offered by emerging magnetic field imagers, such as the Quantum Diamond Microscope, which provide simultaneous wide field-of-view, high spatial resolution vector magnetic field imaging capabilities under ambient conditions. While MCI offers several advantages for non-destructive measurement of microelectronics functional activity, there are many limitations of the technique due to rapid falloff of magnetic fields and loss of high frequency spatial information at large sensor standoff distances. To understand spatial resolution as a function of standoff distance, we consider the problem of using magnetic fields to distinguish (1) between a single wire carrying current $I$ and a pair of wires carrying current $I/2$ in the same direction and (2) between no currents and a pair of wires carrying current $I/2$ in opposite directions. In both cases, we compare performance for a single point measurement, representative of typical magnetometers, to performance for an array of measurements found in emerging magnetic imaging devices. Additionally, we examine the advantage provided by measurement of the full vector magnetic field compared to measurement of a single component. We establish and compare for the first time the theoretical lower bounds on separability based on the wire separation and sensor standoff distance of the magnetic field measurements obtained from traditional and new microelectronics reliability tools.

physics.ins-det↗

arXiv2020

MOSAIC: the high-multiplex and multi-IFU spectrograph for the ELT

MOSAIC is the planned multi-object spectrograph for the 39m Extremely Large Telescope (ELT). Conceived as a multi-purpose instrument, it offers both high multiplex and multi-IFU capabilities at a range of intermediate to high spectral resolving powers in the visible and the near-infrared. MOSAIC will enable unique spectroscopic surveys of the faintest sources, from the oldest stars in the Galaxy and beyond to the first populations of galaxies that completed the reionisation of the Universe--while simultaneously opening up a wide discovery space. In this contribution we present the status of the instrument ahead of Phase B, showcasing the key science cases as well as introducing the updated set of top level requirements and the adopted architecture. The high readiness level will allow MOSAIC to soon enter the construction phase, with the goal to provide the ELT community with a world-class MOS capability as soon as possible after the telescope first light.

astro-ph.IM↗

arXiv2020

Testing the theory of gravity with DESI: estimators, predictions and simulation requirements

Shortly after its discovery, General Relativity (GR) was applied to predict the behavior of our Universe on the largest scales, and later became the foundation of modern cosmology. Its validity has been verified on a range of scales and environments from the Solar system to merging black holes. However, experimental confirmations of GR on cosmological scales have so far lacked the accuracy one would hope for -- its applications on those scales being largely based on extrapolation and its validity sometimes questioned in the shadow of the unexpected cosmic acceleration. Future astronomical instruments surveying the distribution and evolution of galaxies over substantial portions of the observable Universe, such as the Dark Energy Spectroscopic Instrument (DESI), will be able to measure the fingerprints of gravity and their statistical power will allow strong constraints on alternatives to GR. In this paper, based on a set of $N$-body simulations and mock galaxy catalogs, we study the predictions of a number of traditional and novel estimators beyond linear redshift distortions in two well-studied modified gravity models, chameleon $f(R)$ gravity and a braneworld model, and the potential of testing these deviations from GR using DESI. These estimators employ a wide array of statistical properties of the galaxy and the underlying dark matter field, including two-point and higher-order statistics, environmental dependence, redshift space distortions and weak lensing. We find that they hold promising power for testing GR to unprecedented precision. The major future challenge is to make realistic, simulation-based mock galaxy catalogs for both GR and alternative models to fully exploit the statistic power of the DESI survey and to better understand the impact of key systematic effects. Using these, we identify future simulation and analysis needs for gravity tests using DESI.

astro-ph.CO↗

arXiv2020

Use of 3D printing in astronomical mirror fabrication

In this paper we are exploring the possibilities of 3D printing in the fabrication of mirrors for astronomy. Taking the advantages of 3D printing to solve the existing problems caused by traditional manufacturing, two proof-of-concept mirror fabrication strategies are investigated in this paper. The First concept is a deformable mirror with embedded actuator supports system to minimise errors caused by the bonding interfaces during mirror assembly. The second concept is the adaption of the Stress Mirror Polishing (SMP) technique to a variety of mirror shapes by implemented a printed thickness distribution on the back side of the mirror. Design investigations and prototypes plans are presented for both studies.

astro-ph.IM↗

arXiv2018

Astrometry and Occultation predictions to Trans-Neptunian and Centaur Objects observed within the Dark Energy Survey

Transneptunian objects (TNOs) are a source of invaluable information to access the history and evolution of the outer solar system. However, observing these faint objects is a difficult task. As a consequence, important properties such as size and albedo are known for only a small fraction of them. Now, with the results from deep sky surveys and the Gaia space mission, a new exciting era is within reach as accurate predictions of stellar occultations by numerous distant small solar system bodies become available. From them, diameters with kilometer accuracies can be determined. Albedos, in turn, can be obtained from diameters and absolute magnitudes. We use observations from the Dark Energy Survey (DES) from November 2012 until February 2016, amounting to 4292847 CCD frames. We searched them for all known small solar system bodies and recovered a total of 202 TNOs and Centaurs, 63 of which have been discovered by the DES collaboration until the date of this writing. Their positions were determined using the Gaia Data Release 2 as reference and their orbits were refined. Stellar occultations were then predicted using these refined orbits plus stellar positions from Gaia. These predictions are maintained, and updated, in a dedicated web service. The techniques developed here are also part of an ambitious preparation to use the data from the Large Synoptic Survey Telescope (LSST), that expects to obtain accurate positions and multifilter photometry for tens of thousands of TNOs.

astro-ph.EP↗

arXiv2018

First light with HiPERCAM on the GTC

HiPERCAM is a quintuple-beam imager that saw first light on the 4.2m William Herschel Telescope (WHT) in October 2017 and on the 10.4m Gran Telescopio Canarias (GTC) in February 2018. The instrument uses re-imaging optics and 4 dichroic beamsplitters to record ugriz (300-1000nm) images simultaneously on its five CCD cameras. The detectors in HiPERCAM are frame-transfer devices cooled thermo-electrically to -90degC, thereby allowing both long-exposure, deep imaging of faint targets, as well as high-speed (over 1000 windowed frames per second) imaging of rapidly varying targets. In this paper, we report on the as-built design of HiPERCAM, its first-light performance on the GTC, and some of the planned future enhancements.

astro-ph.IM↗

arXiv2016

A Study of Central Galaxy Rotation with Stellar Mass and Environment

We present a pilot analysis of the influence of galaxy stellar mass and cluster environment on the probability of slow rotation in 22 central galaxies at mean redshift $z=0.07$. This includes new integral-field observations of 5 central galaxies selected from the Sloan Digital Sky Survey, observed with the SPIRAL integral-field spectrograph on the Anglo-Australian Telescope. The composite sample presented here spans a wide range of stellar masses, $10.9<$log(M$_{*}/$M$_{\odot})<12.0$, and are embedded in halos ranging from groups to clusters, $12.9<$log(M$_{200}/$M$_{\odot})<15.6$. We find a mean probability of slow rotation in our sample of P(SR)$=54\pm7$percent. Our results show an increasing probability of slow rotation in central galaxies with increasing stellar mass. However, when we examine the dependence of slow rotation on host cluster halo mass we do not see a significant relationship. We also explore the influence of cluster dominance on slow rotation in central galaxies. Clusters with low dominance are associated with dynamically younger systems. We find that cluster dominance has no significant effect on the probability of slow rotation in central galaxies. These results conflict with a paradigm in which halo mass alone predetermines central galaxy properties.

astro-ph.GA↗

arXiv2015

Toward a Comprehensive Model of Snow Crystal Growth: 4. Measurements of Diffusion-limited Growth at -15 C

We present measurements of the diffusion-limited growth of ice crystals from water vapor at different supersaturation levels in air at a temperature of -15 C. Starting with thin, c-axis ice needle crystals, the subsequent growth morphologies ranged from blocky structures on the needle tips (at low supersaturation) to thin faceted plates on the needle tips (at high supersaturation). We successfully modeled the experimental data, reproducing both growth rates and growth morphologies, using a cellular-automata method that yields faceted crystalline structures in diffusion-limited growth. From this quantitative analysis of well-controlled experimental measurements, we were able to extract information about the attachment coefficients governing ice growth under different circumstances. The results strongly support previous work indicating that the attachment coefficient on the prism surface is a function of the width of the prism facet. Including this behavior, we created a comprehensive model at -15 C that explains all the experimental data. To our knowledge, this is the first demonstration of a kinetic model that reproduces a range of diffusion-limited ice growth behaviors as a function of supersaturation.

cond-mat.mtrl-sci↗