Chemical Physics
Reaction mechanisms of sodium carbonate activation in ultra-high-volume slag-cement blends
Samira Hossain, Zhanzhao Li and Kai Gong
Ultra-high-volume slag-cement (UHVS) blends can substantially reduce clinker ratio, but slow slag reaction limits strength development; Na2CO3 is a less caustic and lower-carbon activator for alkali-activated materials, yet its moderate alkalinity can similarly delay strength development. Here, dry-blended Na2CO3 (0-20 wt.% relative to water) was incorporated into a one-part binder with 90 wt.% ground-granulated blast-furnace slag and 10 wt.% Portland cement (PC) to determine whether this integration could mitigate slow strength development in both systems. Compressive strength testing, isothermal calorimetry, time-resolved quantitative X-ray diffraction,
Fourier-transform infrared spectroscopy, aqueous solution analysis, and thermodynamic modeling were combined to determine how Na2CO3 controls phase assemblage, alkalinity, calculated porosity and strength development. At 5-10 wt.% Na2CO3, calcite-dominated carbonate reactions promoted a rapid alkalinity increase and accelerated early-age reaction and strength development; however, subsequent alkalinity decline limited later-age slag reaction and gel formation. In contrast, 20 wt.% Na2CO3 favored substantial early formation of gaylussite and carbonate-AFm phases, which moderated the initial pH rise and delayed early gel accumulation. The larger alkali inventory and evolving phase-solution partitioning helped sustain alkalinity at later ages, enabling continued slag dissolution and gel formation. This pathway produced higher reaction extent and lower calculated porosities, with compressive strength approaching that of neat PC. Screening-level cradle-to-gate life-cycle assessment indicates a 66-72% reduction in global warming potential relative to PC, depending on Na2CO3 production pathways. These results identify carbonate-mediated control of alkalinity persistence as key to sustaining slag reaction in UHVS systems.
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Atmospheric and Oceanic Physics
LoRA Adaptation Strength in Aurora-WRF: Trade-offs in Regional Heavy-Rainfall Forecasting
Boyan Liu and Xiaoyuan Zhang
We investigate how parameter-efficient adaptation of an atmospheric foundation model affects downstream regional precipitation in a controlled Aurora-WRF coupling experiment over the Beijing-Tianjin-Hebei region. A single-step, precipitation-weighted LoRA adaptation of AuroraPretrained is trained on May-September 2020-2021 and selected using 2022 validation data. Five inference-time adaptation strengths are coupled to an unchanged 9-km WRF configuration and compared with a GFS-driven control under common ERA5 initialization and auxiliary-field rules. Evaluation uses 17 initializations in 2023, hourly ERA5 precipitation, and conservative remapping to a fixed 356-
cell, 0.25-degree verification grid. Heavy rainfall is defined exclusively as at least 50 mm in a continuous 24-hour window, advanced hourly. For window starts from 0 to 48 hours, unadapted Aurora attains the highest mean critical success index (CSI), 0.216, compared with 0.144 for the GFS control. An intermediate LoRA strength of 0.75 reduces the Aurora-driven 24-hour precipitation mean squared error by 30.7% and brings pooled frequency Bias from 1.510 to 1.024, but lowers CSI to 0.182. GFS retains the lowest whole-domain error. These results characterize a trade-off between rainfall detection, frequency calibration, and intensity error, showing the importance of evaluating adaptation strength against multiple downstream criteria within a common coupling framework.
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Computational Physics · Cross-listed from cs.CE
Feature tracking in physics-informed neural networks via joint optimization of nonlinear deformation manifolds: application to shocks
Akshay Thakur and Matthew Zahr
Physics-informed neural networks (PINNs) often converge to inaccurate solutions for conservation laws with shocks, because uniformly distributed collocation points undersample localized features and let the residual be dominated by regions that are already well resolved. We propose a feature-tracking PINN (FT-PINN) in which the solution network is defined on a fixed reference domain and composed with a diffeomorphic deformation map from a parameterized nonlinear
manifold. The deformation and solution-network parameters are trained jointly by minimizing the pulled-back conservation-law residual. This lets collocation points concentrate along features of essentially arbitrary geometry, including curved, oblique, and merging shocks, without prior knowledge of their locations. The framework is agnostic to the choice of parameterization. Boundary preservation is enforced exactly through a tangential projection of the displacement, and folding is discouraged by a one-sided penalty on the Jacobian determinant. On four test problems (space-time viscous Burgers with merging shocks, a decelerating Burgers shock, the space-time Euler shock tube, and steady 2D Euler regular shock reflection), FT-PINN resolves shocks at their correct locations with a limited collocation budget. A vanilla PINN with the same architecture, budget, and training either misplaces the shocks or fails to form them.
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Optics · Cross-listed from quant-ph
Frame dependence of Kochen-Specker contextuality for relativistic spin systems
Jonte R. Hance and Ruben Campos Delgado
We ask whether Kochen-Specker contextuality is Lorentz invariant. On the full
Hilbert space, it is, since boosts act unitarily. Restricted to
spin, it is not: momentum acts as an environment applying momentum-dependent Wigner rotations, leaving
spin observables unsharp and breaking the operator identities behind state-independent contextuality. For
spin $1/2$ one scalar governs the loss, the packet average $\Delta$ of $\sin^2(\theta_W/2)$ (where $\theta_W$ is the Wigner angle); for any
spin, a ray degrades at a rate set by its
spin variance transverse to the boost. In the
spin-only account, the Free Will
Theorem's
SPIN axiom fails for every $\Delta>0$; the Yu-Oh set loses contextuality between $\Delta\approx0.008$ and $0.016$, with state independence going first; the Peres-Mermin square loses contextuality at $\Delta\approx0.059$ or $0.074$ by orientation; singlet CHSH loses nonlocality for $\Delta\geq0.159$; and a GHZ-state OR gate loses Raussendorf's contextuality certificate at $\Delta\approx0.206$. A $q$-plate implements the channel exactly on
photon polarisation, with a retardation in place of the rapidity, so the nonlocality, GHZ and aimed-source results are testable without motion.
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Fluid Dynamics
Exact Periodic Solutions of the Forced Incompressible Navier-Stokes Equations in Arbitrary Dimensions
R. K. Michael Thambynayagam
Exact periodic solutions of the unforced incompressible Navier-Stokes equations require the convective field to be a pure gradient, absorbed into the pressure; for the cyclic trigonometric families studied previously this occurs only at isolated phases and, over the range so far classified, only at n = 3 and 4. The forced problem, we show, admits an exact construction for every phase vector and every n >= 3. For an n-parameter family of two-term cyclic fields, solenoidal and a
Laplacian eigenfunction, the transverse part U^0 of its convective field serves as the body force, giving a closed-form solution with velocity, pressure and force explicit. We prove closed forms for the pressure and for both magnitudes, in every dimension and with no upper bound on n: the forcing is never trivial, and the L^2 magnitude of U^0 stands to that of the whole convective field in the constant ratio 2*sqrt(2)/3, whatever the dimension, phase or units. The forcing does no net work, so the kinetic energy decays purely viscously at arbitrary Reynolds number, giving an exact
benchmark in any dimension. A numerical study of vorticity concentration at zero applied work uses it as a reference, and shows that apparent saturation can be a resolution artefact.
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Fluid Dynamics
CraftSPH: A high-accuracy and composable differentiable SPH solver implemented in PyTorch
Gen Matono, Shujiro Fujioka and Mayuko Nishio
Smoothed Particle Hydrodynamics (SPH) is well suited to a range of problems, particularly those involving large deformations in fluid dynamics. In recent years, in addition to the advancement of SPH formulations, the development of differentiable solvers has also progressed. However, unified frameworks that flexibly accommodate diverse numerical schemes, including advanced and implicit methods, while supporting continuous extension and updating remain limited. In this study, a high-accuracy and composable differentiable SPH solver, CraftSPH, has been developed. In CraftSPH, major computational operations are implemented as independent modules, which can be combined according to the intended purpose of constructing a solver. This design enables different computational schemes to be intuitively constructed from combinations of common components and allows newly developed high-accuracy discretization methods to be flexibly incorporated and extended. Furthermore, each module supports automatic differentiation, enabling applications to physical-parameter estimation and to optimization that combines SPH solvers with deep learning models. Accordingly, CraftSPH provides an SPH computational framework in which high-accuracy discretization, explicit and implicit computations, and automatic differentiation can be handled in a unified and extensible manner. To demonstrate the applicability of CraftSPH to a wide range of problems, forward analyses are conducted for Poiseuille flow, two- and three-dimensional dam-break problems, and a rising-bubble problem. In addition, the performance of automatic differentiation is evaluated through inverse problems involving parameter estimation for the Taylor-Green
vortex and lid-driven cavity flow.
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Physics Education
CMS Open Data Visualization with FireworksWeb
Yuxiao Wang, Alja Mrak-Tadel, Matevž Tadel et al. (+4 more)
FireworksWeb is a web-based event display utilizing a C++ ROOT-Eve backend with SAPUI5 frontend for interactive 3D visualization of particle physics events directly in the browser. Building upon ROOT-Eve and RenderCore, it eliminates local software installation while maintaining professional-grade event display capabilities. FireworksWeb is currently deployed for live event monitoring in the CMS control room, demonstrating its reliability for real-time data visualization in production environments. The CMS experiment has made a large amount of collision
datasets publicly available through the CERN Open Data portal, which are the valuable resources for education, outreach, and research. We have extended FireworksWeb to enable visualization of these open
datasets, making particle physics data accessible to diverse audiences without requiring local software installation. Our prototype implementation includes standalone cameras, event filters, configurable collection settings, and projection controls with fish-eye distortion for cylindrical geometries. The web architecture communicating via CGI enables
dataset queries and filtered event selection based on physics criteria. We demonstrate the prototype with CMS Open Data events, discuss the technical architecture, and outline applications for education, public outreach, and exploratory physics analysis.
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All authors Yuxiao Wang, Alja Mrak-Tadel, Matevž Tadel, Avi Yagil, Mario Masciovecchio, Dmytro Kovalskyi, Sergey Linev
Popular Physics
The "Gate of Fate" - a quantum gaming concept
Deeptanshu Malu, Deevyanshu Malu and Bhaskaran Muralidharan
Quantum physics underpins the emerging
quantum technology era, yet its counterintuitive principles remain challenging for beginners.
Video games offer a promising medium to make these principles accessible through an interactive experience. Conventional gameplay, even when featuring extraordinary abilities of the protagonist, largely draws on classical intuition, shaping the player's reflexes through familiar expectations of motion, action, and consequence. Here, we propose extending these reflexes into the
quantum domain by
embedding superposition,
unitary evolution,
quantum gates, and measurement into the game. Central to this approach is a new controller action, the Gate of Fate, which allows players to engage in
quantum operations through their gaming decisions. We demonstrate the concept in a Tetris-based prototype, with progressively structured levels that introduce
quantum operations and their consequences. We also outline extensions to role playing games, in which the behavior and difficulty of antagonists can respond to player's developing
quantum gaming reflexes. By turning abstract
quantum concepts into actionable game mechanics, this approach aims to cultivate
quantum intuition through play, offering opportunities for both
quantum pedagogy and more sophisticated game design.
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Atmospheric and Oceanic Physics · Cross-listed from stat.AP
Suitable Measures for the Potential Operational Utility of AI NWP Rainfall Forecasts Over Africa
Shruti Nath, Docko Sow, Koomi Toussaint Amoussouvi et al. (+5 more)
Artificial intelligence (AI)-based weather prediction is approaching the skill of physical numerical weather prediction (NWP) systems at a fraction of the computational cost. This is particularly promising for Africa, where rainfall extremes are intensifying and many forecasting centres lack the infrastructure to run physical models at extended lead times. We present a calibrated comparison of GraphCast, GenCast and the Functional Generative Network (FGN) against the physical NWP model IFS for rainfall prediction across Africa. Deterministic and probabilistic forecasts are postprocessed using Isotonic Distributional Regression and evaluated with the Continuous Ranked Probability Score against IMERG, RFEv2 and CHIRPS across seasons, wet and dry regimes, elevation zones and lead times. All models retain skill beyond climatology across most seasons and at extended lead times. AI models generally outperform IFS in wet regions, whereas IFS performs better in dry, high-elevation areas, where its finer resolution better represents orographic controls on rainfall. Across observational
datasets and seasons, AI models achieve a median improvement of approximately 5% over IFS. GraphCast achieves calibrated skill comparable to the ensemble-based FGN, although FGN provides greater significant skill at longer lead times. These results highlight the potential of calibrated AI weather prediction to provide accessible and computationally efficient rainfall forecasts across Africa, while demonstrating the continuing importance of spatial resolution, ensemble design and regional characteristics.
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All authors Shruti Nath, Docko Sow, Koomi Toussaint Amoussouvi, Fenwick Cooper, Josiah Kiarie Kimani, John Bagiliko, Florian Pappenberger, Rendani Mbuvha
Fluid Dynamics · Cross-listed from cs.CV
HGPTrans: Hierarchical Graph-Pooling Transolver for Automotive Aerodynamic Drag Coefficient Prediction
Bo Liu, Fengli Zhang, Qiuli Luo et al. (+2 more)
Accurate and rapid prediction of the aerodynamic drag coefficient ($C_D$) is essential for vehicle design, particularly during early-stage design, where many candidate geometries must be evaluated. Although computational fluid dynamics (CFD) provides reliable aerodynamic estimates, its high computational cost limits large-scale design exploration. This paper proposes the hierarchical graph-pooling Transolver (HGPTrans), which combines hierarchical graph pooling with Transolver-based
attention to directly predict $C_D$ from vehicle surface meshes. Motivated by the fact that vehicle aerodynamics depends on both local
geometric features and long-range interactions among spatially distant surface regions, HGPTrans integrates three complementary components. Graph isomorphism convolutions encode discriminative local geometry, Transolver-style slice
attention captures global interactions with linear computational complexity, and information-redundancy-aware hierarchical pooling progressively removes redundant nodes while preserving informative
geometric structures. The model is trained and evaluated on the large-scale DrivAerNet and DrivAerNet++
datasets, where it achieves the lowest mean absolute error and mean squared error among the evaluated baselines. Its generalization capability is further assessed through transfer learning on a real-vehicle
dataset containing both sedans and sport utility vehicles (SUVs), achieving relative $L_1$ errors of 1.56% (sedans) and 2.12% (SUVs) with an inference time of approximately $0.293$ s per vehicle. This corresponds to an acceleration of several orders of magnitude relative to high-fidelity CFD while keeping the predicted drag coefficients within a few percent of the CFD reference. Ablation studies confirm each component's contribution and reveal the effects of depth and pooling ratio.
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All authors Bo Liu, Fengli Zhang, Qiuli Luo, Lianrui Nie, Wenjiang Wang
Medical Physics
MBFormer: Microbubble Transformer for 3D Time-Series Data Processing to Improve Bound Bubble Detection in Nonde-structive Ultrasound Molecular Imaging
Jihye Baek, Jeong Hoon Lee, Hoda Hashemi et al. (+4 more)
Development of nondestructive ultrasound molecular imaging (UMI) is essential for early cancer detection through real-time screening using clinical ultrasound systems. Current techniques face challenges in accurately detecting targeted microbubbles (MBs) bound to specific biomarkers, primarily due to false-positive detections of unbound free-floating MBs. We propose a
transformer model for time-series
video processing to improve the differentiation of bound MBs. We propose a hierarchical
transformer, termed MBFormer (microbubble
transformer), featuring a positional-
embedding-free encoder and a lightweight decoder. Leveraging
attention within 3D spatio-temporal data to effectively capture stationary signals from bound MBs while suppressing nonstationary signals from unbound MBs. Since MBs appears as relatively small textures compared with conventional segmentation targets in medical imaging, such as organs and tumors, we optimized the model with two hierarchical layers, each with an
attention block, to process ultrasound
video data. The network outputs the molecular signal amplitude to visualize fine MB textures. Performance was evaluated using an in vivo breast cancer model, compared against a prior CNN-based UMI method and SegFormer3D baseline, a representative 3D
transformer. MBFormer (AUC = 0.943) outperformed both CNN (AUC = 0.897) and SegFormer3D (AUC = 0.766) in detecting bound MBs. The CNN showed residual molecular signal from free MBs in the cardiac chambers, whereas SegFormer3D failed to detect fine MB textures. Overall, MBFormer demonstrated enhanced detection of bound MBs while suppressing free MBs and achieved a frame rate of 16.7 to 18.1 FPS, demonstrating its potential for real-time application. We anticipate that this
transformer-based UMI model can facilitate real-time, free-hand nondestructive UMI in clinical systems.
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All authors Jihye Baek, Jeong Hoon Lee, Hoda Hashemi, Arutselvan Natarajan, Farbod Tabesh, Ramasamy Paulmurugan, Jeremy J. Dahl
Atmospheric and Oceanic Physics
Weather and Climate Without Fluid Mechanics
Thomas D. DeWitt
Even if it were possible, attempting to model the motion of a fluid in a pipe by tracking each individual molecule would be an extraordinary waste of computation. The emergent laws of fluid mechanics accomplish the same goal on a laptop instead of a datacenter. But a sufficiently complex fluid such as in Earth's atmosphere again requires a datacenter, this time to resolve the many contortions in the fields making up our weather. Might there be some analogous set of emergent laws that would represent the effect of these perturbations without needing their explicit representation on a numerical grid? We argue that the existence of a
symmetry principle implies such laws are likely to exist. The atmosphere is scale invariant, implying that the observed structure at one scale is nothing but a stretched version of that at any other scale, at least within the invariant regime. Though some emergent laws built on scale invariance exist, they are built on a challenging ontological foundation, making them hard to develop further. We propose a path forward, first by introducing a new fundamental building block of the atmosphere that we term a “turbulon”, inspired by a lesser-known generalization of
turbulence theory that accounts for buoyancy. We then construct the Superposition of Turbulons and Eddies Atmospheric Model (STEAM), and show that simulated atmospheric volumes are broadly plausible when compared to state-of-the-art hydrodynamic simulation output. Some aspects of STEAM clearly need improvement, though other statistics reproduce observations better than hydrodynamic models, and visualizations of simulated clouds are strikingly realistic. We estimate STEAM's computational cost to be up to a million times less than hydrodynamic models, suggesting that the Navier-Stokes equations may not, in fact, be required for simulation and prediction of Earth's weather and
climate.
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Fluid Dynamics
Dual Laser and Shadow Particle Image Velocimetry for Multiscale Fluid-Structure Interactions
Adrian Herrera-Amaya and Monica M. Wilhelmus
Experimental Fluid-Structure Interaction (FSI) often involves phenomena that occur simultaneously across vastly different spatial and temporal scales. For example, the separation bubble behind a cylinder and the
vortex interactions in its wake, or the flow produced by an individual swimmer and the flow generated by the movement of the entire swarm. Imaging-based flow studies of such FSI phenomena are challenging and often lead to separate analyses of different flow regions. This work introduces simultaneous shadow and
laser-based Particle Image Velocimetry (PIV) to obtain concurrent near-wall and far-field flow velocity measurements. The experimental setup developed here enables the first simultaneous comparison between shadow- and
laser-based PIV, demonstrating that shadow velocimetry can achieve accuracy comparable to
laser-based measurements at the macro scale. We then use this new combined flow measurement technique to fully capture the velocity field generated by a shrimp-inspired underwater
robot, employing
laser PIV to record the flow surrounding the
robot and shadow PIV to focus on the actuators, imaging regions that the
laser technique cannot resolve due to wall reflections and shadows behind the structures. The simultaneous application of shadow- and
laser-based PIV offers a new experimental avenue for gaining insights into the physics of FSI phenomena.
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Accelerator Physics · Cross-listed from eess.SY
From Routing Delay Shifts to Silent Data Corruption: Neutron-Induced SEU Effects in AXI-Based Zynq UltraScale+ MPSoCs
Mostafa Darvishi
SRAM-based FPGA system-on-chip devices are vulnerable to single-event upsets (SEUs) in configuration
memory, which may perturb programmable routing resources and degrade communication fabrics. In modern Zynq UltraScale+ MPSoCs, such routing disturbances can introduce small propagation delay shifts that remain logically transparent yet compromise AXI-based data transfers and lead to silent data corruption. Although routing delay degradation and AXI interconnect failures have been studied independently, their experimental correlation under
neutron irradiation has not been established. This work presents a cross-layer investigation on a ZCU104 platform integrating routing-dominated delay sensors with an AXI interconnect
benchmark comprising replicated accelerators.
Neutron irradiation experiments were conducted on the fully operational system, while a frame-level configuration fault injector implemented via the internal configuration access port enables controlled upset emulation. Measured routing delay events are statistically correlated with communication failures, and cross-sections for both timing shifts and AXI malfunctions are derived. The results experimentally demonstrate how
neutron-induced routing perturbations propagate into system-level silent data corruption in UltraScale+ MPSoCs, providing insight for resilience-oriented AXI-based design in
neutron-rich environments.
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Optics
Energy-selective control of noise-assisted multipulsing by weak optical seeding in the dissipative-soliton-resonance regime
Vladimir L. Kalashnikov, Alexander Rudenkov, Evgeni Sorokin et al. (+1 more)
We study pulse-number selection in a
stochastic cubic–quintic complex Ginzburg–Landau model of a weakly seeded, normal-dispersion
laser in the dissipative-
soliton-resonance (DSR) regime. Without noise, a single pulse and pulse pairs persist at the same control parameters, and the total energy of an $N$-pulse state follows a ladder constructed from the single-pulse branch. The final energies of noisy trajectories lie close to the same ladder. Multipulsing therefore does not necessarily lose the single-pulse solution. It can reflect which coexisting state the noisy dynamics reaches. Optical seed injection suppresses energy-dependent multipulsing and, at larger seed power, produces a nonmonotonic DSR energy window. An energy–noise scan shows that energy dominates the multipulse probability, whereas additive noise produces only a modest trend common to all energies, without a noise optimum. The noise-induced formation statistics therefore do not establish canonical
stochastic resonance or escape from a pre-existing
soliton. Coherent control by a weak monochromatic seed extends predominantly single-pulse operation over a noticeably broader energy window, enhancing dissipative-
soliton energy scalability. Within an adiabatic approximation, equal energy sharing among coexisting pulses is stable wherever the single-pulse energy grows less than proportionally with the control energy, as it does over the sampled DSR range. Energy exchange between the pulses then relaxes up to about 200 times more slowly than their total energy.
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All authors Vladimir L. Kalashnikov, Alexander Rudenkov, Evgeni Sorokin, Irina T. Sorokina
Optics
Skyrmion topology of scalar waves in real and momentum space
Nai H. Kwong, Ewan M. Wright, Jan Wingenbach et al. (+4 more)
Generalizations of
magnetic skyrmions to the context of other areas of physics, such as plasmonics, optics (including microcavity polaritons) and even water waves have become an important research topic. However, the generalization of the skyrmion number in such systems is still the subject of active research. While
magnetic skyrmions textures are inherently formed by vectors (
magnetic spin vectors), recent developments in other areas of physics have generalized the concept of skyrmions and skyrmionic patterns to the case in which scalar fields are used to define 3-component pseudo-
spin vectors. This gives rise to a wide variety of skyrmion-like textures, depending on the detailed functional forms of the scalar fields. For example, steady-state outgoing waves in driven-dissipative polariton systems fail to have a well-defined global skyrmion number in real (configuration) space but can have one in momentum space. We classify the skyrmion integral for various scalar fields with different asymptotic behaviors in real space as well as momentum space, and in both cases give conditions for the skyrmion number not to exist, or be integer, half-integer or non-integer. Examples of physical systems to which our analysis can be applied include polariton condensates, water waves and gain-guided
lasers.
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All authors Nai H. Kwong, Ewan M. Wright, Jan Wingenbach, Roman Lebs, Harald Giessen, Stefan Schumacher, Rolf Binder
Atmospheric and Oceanic Physics
Toward Actionable Predictability of Tropical Mesoscale Convective Systems through Large-scale Recurrent Environments in Northern South America
Vanessa Robledo, John F. Mejía, Jhayron S. Pérez-Carrasquilla et al. (+2 more)
Mesoscale convective systems (MCSs) produce much of the extreme precipitation, floods, and landslides that affect northern South America (NOSA), yet remain difficult to anticipate because they are strongly governed by local processes. On the night of 31 March 2017, an MCS triggered a catastrophic flash flood in Mocoa, Colombia —an event whose severity was not foreseen with sufficient lead time for emergency action, which typically requires several hours to a couple of days. Motivated by this disaster, this study investigates whether such events emerge within recurrent environments that provide a source of large-scale predictability, rather than as isolated and inherently unpredictable occurrences. We combine a 21-year (2001–2021) MCS tracking
dataset, ERA5 reanalysis, equatorial Rossby (ER) wave diagnostics, and a Self-Organizing Map (SOM) to identify recurrent environments associated with MCS occurrence in the Orinoco–Amazon basin. The Mocoa environment maps onto one of nine recurrent configurations characterized by enhanced low-level moisture, neutral Orinoco low-level jet conditions, favorable upper-level circulation, and strong ER-wave activity. We further trace convective precursors to an upstream source region over the Guiana Highlands up to 24~h before arrival in the Mocoa region. Together, these results show that the Mocoa event was not climatologically exceptional but a high-impact expression of a recurrent, partly predictable large-scale regime, demonstrating that portion of MCS predictability arise from recurring large-scale conditions that can be monitored hours to days in advance, and support hazardous weather forecasting in NOSA and other tropical regions.
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All authors Vanessa Robledo, John F. Mejía, Jhayron S. Pérez-Carrasquilla, Mohamed Abdelkader, Humberto Vergara
Plasma Physics
Demonstration of H-mode Error Field Identification in a Single Discharge via Island Healing
E.M. Bursch, A. Xie, J.L. Barr et al. (+15 more)
Non-disruptive compass scan error field identification via island healing is demonstrated on KSTAR in a single H-mode discharge. This method can allow for up to a 75% reduction in the operational time necessary for a complete compass scan by reducing the required discharges from four to one. Previous experiments on DIII-D, JET, and MAST-U have demonstrated the technique in Ohmic and L-mode but required multiple discharges and strong real-time n=1 magnetics diagnostics close to the
plasma that are unlikely to be viable for future devices. The results presented here were made possible by implementing a new set of triggering
algorithms relevant to fusion pilot plant operation into the KSTAR
plasma control system, along with KSTAR's strong RMPs and long pulse lengths. This represents a significant step towards the disruption-free error field identification method being demonstrated for deployment on future disruption-averse tokamaks, including ITER and fusion pilot plants. Steps needed to close remaining gaps to viability are addressed.
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All authors E.M. Bursch, A. Xie, J.L. Barr, S.K. Kim, N.C. Logan, S.M. Yang, J.G. Bak, W. Choi, Q. Hu, J.W. Juhn, J. Kim, J. Lee, N. Leuthold, J.K. Park, C. Paz-Soldan, L. Piron, G. Seo, J. Seo
Medical Physics · Cross-listed from eess.IV
PGDM-MRSRGAN: Physics-Guided Degradation Model with an SRGAN Framework for Magnetic Resonance Image Super-Resolution: Applications in Low-Field MRI
Yashwant Kurmi, Charlotte R. Sappo, Sai Abitha Srinivas et al. (+3 more)
Magnetic Resonance Imaging (MRI) often suffers from low signal-to-noise ratio (SNR) and limited spatial resolution, which compromise clinical precision. This study aims to address these challenges by developing a physics-guided degradation model (PGDM) and a novel deep learning framework,
Magnetic Resonance Super-Resolution GAN Imaging (MRSRGAN), for improved super-resolution reconstruction of MRI images. The proposed approach consists of two stages: (1) a physics-guided degradation model that simulates low-field MRI conditions by incorporating artifacts such as blur, B0 inhomogeneity, chemical shift effects, down-sampling, and noise to generate paired low-resolution (LR) and high-resolution (HR) training
datasets; and (2) an MRSRGAN-based reconstruction network utilizing a hybrid loss function, including modified spectral angle mapper (MSAM) and sharpened ground truth images based on de-convolution of slice profile-based kernels to restore high-SNR and high-resolution images. Experimental validation demonstrates that the proposed method achieves superior spatial resolution, enhanced SNR, improved peak signal-to-noise ratio (PSNR), higher structural similarity index (SSIM), reduced MSAM error, and better non-reference image quality evaluation (NIQE) metrics. Furthermore, it effectively reduces artifacts and demonstrates robustness on real MRI
datasets. The MRSRGAN framework, guided by the physics-informed degradation model, provides a significant improvement in MRI image quality, enhancing spatial resolution and diagnostic accuracy. Its demonstrated robustness and effectiveness on real MRI
datasets highlight its potential to resolution enhancement for precise diagnostic imaging.
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All authors Yashwant Kurmi, Charlotte R. Sappo, Sai Abitha Srinivas, Selin D. Kandemir, Malvika Viswanathan, Zhongliang Zu
Atmospheric and Oceanic Physics
Understanding Perturbed Parameter Ensemble Sensitivities Using A Contrastive Learning Approach
Da Fan, David John Gagne II, Gregory S Elsaesser et al. (+5 more)
Perturbed parameter ensembles (PPEs) reveal how physics parameters affect
climate simulations, but interpreting parameter sensitivities across multivariate, spatially structured outputs remains challenging, particularly when calibrating models against observations. We develop an explainable contrastive learning model that maps 5 monthly cloud and radiation fields into a shared representation space. We train the model on the fields of two 100-member Community Atmosphere Model version 6 (CAM6) PPEs, spanning 34 parameters, that only differ in the warm rain microphysics scheme: KK2000, the default bulk microphysics scheme, and TAU-ML, a neural network emulator of a bin microphysics scheme. The learned representations separates two PPEs with over 94% linear classification accuracy while preserving the seasonal variability and ensemble spread due to parameter perturbations. In the shared representation space, the representations of satellite observations occupy the same low-dimensional
manifold as the PPEs but are displaced from them most strongly during boreal spring and autumn. TAU-ML PPE has a lower distance to observations compared to KK2000 in the representation space. Integrated Gradients attributions highlights the contributions in subtropical low-cloud regions, Northern and Southern Hemisphere storm track regions, and tropical convection regions to differences between PPEs and observations. Regional attributions correlate most strongly with parameters associated with cloud microphysics, boundary layer
turbulence, and deep convection. These results demonstrate that explainable representations of
climate fields can attribute model differences to specific variables, regions, seasons, and physical parameters.
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All authors Da Fan, David John Gagne II, Gregory S Elsaesser, Brian Medeiros, Addisu G Semie, Qingyuan Yang, Akila Sampath, Subashree Venkatasubramanian
Data Analysis and Statistics · Cross-listed from hep-ph
A Phase-Space Inclusive Figure of Merit Based in Optimal Transport for Validating Monte Carlo Reweightings
Rishabh Jain, Lauren Hay, Matt LeBlanc et al. (+1 more)
Validating whether the underlying physics of a model has been retained after a full phase-space reweighting poses a unique challenge. Often, validation relies on comparing histograms of 1D observables; however, this can mask correlations and biases in the complete prediction. We present a novel, unbinned approach to comparing the performance of such reweighting schemes based on the "Cross-Section-Mover's Distance", an application of Optimal Transport that quantifies the work required to transform one theoretical prediction into another and enables an interpretation of results in terms of metric spaces. We demonstrate its utility when benchmarking various reweighting schemes that mitigate the effects of negative weights in a Monte Carlo simulation. This approach can be broadly applied in other scenarios where biases in full phase-space reweighting schemes should be studied in an unbinned way.
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All authors Rishabh Jain, Lauren Hay, Matt LeBlanc, Jennifer Roloff
Instrumentation and Detectors
Point-cloud generative models for fast calorimeter simulation across particles and geometries
Thorsten Buss, Henry Day-Hall, Frank Gaede et al. (+7 more)
Detailed Geant4 simulation of calorimeter showers is the largest single computing cost of
collider experiments, and the High-Luminosity LHC will need about ten times more simulated events than are currently produced. We summarise recent progress in generative point cloud fast simulation for highly granular calorimeters. CaloClouds3 generates
photon (electromagnetic) showers, is geometry-independent, and runs on average about 120x faster than Geant4 on a single CPU. CaloHadronic uses
transformer attention to extend the point cloud
diffusion approach to pion (hadronic) showers spanning the electromagnetic and hadronic calorimeters. AllShowers unifies twelve particle types in a single model with far fewer parameters than the specialised baselines while matching or exceeding their fidelity. We close with cross-geometry transfer learning, which needs two to three orders of magnitude fewer training showers while preserving the generative performance.
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All authors Thorsten Buss, Henry Day-Hall, Frank Gaede, Gregor Kasieczka, Katja Krüger, Anatolii Korol, Thomas Madlener, Peter McKeown, Martina Mozzanica, Lorenzo Valente
Computational Physics · Cross-listed from cs.CE
Perspectives on Sustainable Computational Science and Engineering
Julia Kowalski, Ann S. Almgren, Achim Basermann et al. (+43 more)
Computational Science and Engineering (CSE) combines expertise at the intersection of engineering, applied mathematics, and computer science to form powerful methods for model-based design and model-based decision support across disciplines. Today, CSE methods and tools have an impact as an enabling technology in the development of increasingly sustainable products, processes, and operations. However, sustainability is rarely considered holistically in the context of CSE itself. This paper develops a perspective on sustainability in CSE by distinguishing CSE as an enabler of sustainability in application domains from sustainability within CSE itself, which rests on two complementary pillars: sustainable computing and sustainable software. We present illustrative examples for these pillars and derive recommendations and best practices for CSE stakeholders. Developing an understanding of how sustainability can create added value in CSE is an important future direction for the field. This calls for a concerted effort within the CSE community to define measurable outcomes and best practices that embed sustainability as a core design principle rather than an afterthought.
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All authors Julia Kowalski, Ann S. Almgren, Achim Basermann, Alan Correa, Immo Huismann, Uwe Naumann, Hendrik Ranocha, Matthias Rauter, Jens Saak, Michael Schlottke-Lakemper, Robert Speck, Manuel Torrilhon, Mario Wolter, Niklas von der Assen, Maria Guadalupe Barrios Sazo, Dipankul Bhattacharya, Marcel Bock, Faras Brumand-Poor, Dirk Brömmel, Semih Burak, Sara Grundel, Christiane Helzel, Bruce Hendrickson, Robert Klöfkorn, Katharina Kormann, Zahra Lakdawala, Hannah Lanzrath, Camilla Lüttgens, Anna Matuszy`nska, Matthias Meinke, Abigail Morrison, William S. Moses, Georgii Oblapenko, Marcel Pfeiffer, Jana Sasse, Katharina Schmitz, Miriam Schulte, Anna Schwarz, Pit Steinbach, Ingo Steldermann, Anton Stratmann, Lambert Theisen, Joseph De Veaugh-Geiss, Tim Wegmann, Gabriel Wittum, Anil Yildiz
Plasma Physics · Cross-listed from astro-ph.GA
Saturation Mechanism of Cosmic Ray Streaming Instabilities with a Shell Distribution
Mohamad Shalaby, Rouven Lemmerz and Christoph Pfrommer
Cosmic rays (CRs) drive galactic winds and regulate
galaxy growth while contributing to heating the central cooling
plasma in dense
galaxy clusters, making the nonlinear saturation of the instabilities that govern their transport a problem of central importance in astrophysics. Using fully kinetic particle-in-
cell (PIC) simulations, we find that the streaming instability driven by CRs with a shell momentum distribution saturates through gyro-phase bunching around driven Alfvén waves. Fluid-PIC (FPIC) simulations with both ideal and Landau closures reproduce the PIC saturation amplitude, and the same gyro-phase bunching mechanism, indicating that nonlinear Landau damping (NLLD) does not determine the saturation level at our simulation parameters ($v_{\rm A} = 0.01c$, $n_{\rm CR}/n_i = 0.01$). Notably, the CR ions fully isotropize in the Alfvén-wave frame in all simulations, including the FPIC run with an ideal closure in which NLLD is entirely absent, showing that this isotropization does not require NLLD. We additionally confirm, using the FPIC ideal closure, that this saturation amplitude is unchanged across a tenfold increase in domain size, indicating that the mechanism is local rather than dependent on domain-scale processes. Together, these results indicate that, at least in this regime, NLLD is not the dominant saturation mechanism for the CR streaming instability, in tension with common assumptions built into CR transport models used in
galaxy formation and interstellar medium simulations. Whether this conclusion extends to the lower CR densities and Alfvén speeds characteristic of the interstellar medium remains an open question that we address in an upcoming work.
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