pwc-archive/papers-with-abstracts huggingface dataset:
Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations
Authors: Henrik Christiansen, Takashi Maruyama, Federico Errica, Viktor Zaverkin, Makoto Takamoto, Francesco Alesiani
Date: 2025-03-26 00:00:00

We present an end-to-end differentiable molecular simulation framework (DIMOS) for molecular dynamics and Monte Carlo simulations. DIMOS easily integrates machine-learning-based interatomic potentials and implements classical force fields including particle-mesh Ewald electrostatics. Thanks to its modularity, both classical and machine-learning-based approaches can be easily combined into a hybrid description of the system (ML/MM). By supporting key molecular dynamics features such as efficient neighborlists and constraint algorithms for larger time steps, the framework bridges the gap between hand-optimized simulation engines and the flexibility of a PyTorch implementation. The superior performance and the high versatility is probed in different benchmarks and applications, with speed-up factors of up to $170\times$. The advantage of differentiability is demonstrated by an end-to-end optimization of the proposal distribution in a Markov Chain Monte Carlo simulation based on Hamiltonian Monte Carlo. Using these optimized simulation parameters a $3\times$ acceleration is observed in comparison to ad-hoc chosen simulation parameters. The code is available at https://github.com/nec-research/DIMOS.
Single-molecule trajectories of reactants in chemically active biomolecular condensates
Authors: Stefano Bo, Lars Hubatsch, Frank Jülicher
Date: 2025-03-25 00:00:00

Biomolecular condensates provide distinct chemical environments, which control various cellular processes. The diffusive dynamics and chemical kinetics inside phase-separated condensates can be studied experimentally by fluorescently labeling molecules, providing key insights into cell biology. We discuss how condensates govern the kinetics of chemical reactions and how this is reflected in the stochastic dynamics of labeled molecules. This allows us to reveal how the physics of phase separation influences the evolution of single-molecule trajectories and governs their statistics. We find that, out of equilibrium, the interactions that enable phase separation can induce directed motion and transport at the level of single molecules. Our work provides a theoretical framework to quantitatively analyze single-molecule trajectories in phase-separated systems.
Supply chain network rewiring dynamics at the firm-level
Authors: Tobias Reisch, András Borsos, Stefan Thurner
Date: 2025-03-26 00:00:00

Supply chain networks (SCN) form the structural backbone of any society. They constitute the societal metabolism that literally produces everything for everybody by coordinating practically every single person on the planet. SCNs are by no means static but undergo permanent change through the entry and exit of firms and the re-arrangement of supply relations. Here we use a unique dataset to explore the temporal evolution of firms and their supplier-buyer relations of a national SCN. Monthly reported value added tax data from Hungary from 2014 to 2022 allows us to reconstruct the entire economy with 711,248 companies and 38,644,400 connections, covering practically every re-structuring event of an entire economy at firm-level resolution. We find that per year about 25\% of firms exit the SCN while 28\% new ones enter. On average, 55\% of all supply-links present in one year will not be present in the next. We report the half-life time of supply-links to be 13 months. New links attach super-preferentially to firms with a probability, $p(i)\propto k_i^{1.08}$, with $k_i$ firm $i$'s number of supply-connections. We calibrate a simple statistical network generation model that reproduces the stylized characteristics of the dominant Hungarian SCN. The model not only reproduces local network features such as in- and out-degree distributions, assortativity and clustering structure, but also captures realistic systemic risk profiles. We discuss the present model in how rewiring dynamics of the economy is essential for quantifying its resilience and to estimate shock propagation.
Stem-cell differentiation underpins reproducible morphogenesis
Authors: Dominic K Devlin, Austen RD Ganley, Nobuto Takeuchi
Date: 2025-03-25 00:00:00

Morphogenesis of complex body shapes is reproducible despite the noise inherent in the underlying morphogenetic processes. However, how these morphogenetic processes work together to achieve this reproducibility remains unclear. Here, we ask how morphogenetic reproducibility is realised by developing a computational model that evolves complex morphologies. We find that evolved, complex morphologies are reproducible in a sizeable fraction of simulations, despite no direct selection for reproducibility. Strikingly, reproducible morphologies had also evolved stem-cell systems. We show that reproducibility is caused by a morphogenetic division of labour based on these stem-cell systems, where moving, dividing stem cells ``shape'' morphologies and irreversibly differentiate into stationary, non-dividing cells. These results suggest that stem-cell systems observed in natural development play fundamental roles in morphogenesis in addition to their known role of producing specialised cell types. This previously-unrecognised role of stem-cell systems has major implications for our understanding of how morphologies are generated and regenerated.
The Misinterpretable Evidence Conveyed by Arbitrary Codes
Authors: Guido Fioretti
Date: 2025-03-23 00:00:00

Evidence Theory is a mathematical framework for handling imprecise reasoning in the context of a judge evaluating testimonies or a detective evaluating cues, rather than a gambler playing games of chance. In comparison to Probability Theory, it is better equipped to deal with ambiguous information and novel possibilities. Furthermore, arrival and evaluation of testimonies implies a communication channel. This paper explores the possibility of employing Evidence Theory to represent arbitrary communication codes between and within living organisms. In this paper, different schemes are explored for living organisms incapable of anticipation, animals sufficiently sophisticated to be capable of extrapolation, and humans capable of reading one other's minds.
EEG relative phase-based analysis unveils the complexity and universality of human brain dynamics: integrative insights from general anesthesia and ADHD
Authors: Athokpam Langlen Chanu, Youngjai Park, Younghwa Cha, UnCheol Lee, Joon-Young Moon, Jong-Min Park
Date: 2025-03-12 00:00:00
Tasks: EEG, Time Series

Understanding brain wave patterns is fundamental to uncovering neural information processing mechanisms, making quantifying complexity across brain states an important line of investigation. We present a comprehensive analysis of the complexity of electroencephalography (EEG) signals, integrating data from seven distinct states experienced by participants undergoing general anesthesia, and resting-state recordings from individuals with inattentive-type ADHD alongside healthy control groups. Departing from prior studies that primarily focus on EEG amplitude dynamics, we adopt a novel relative phase approach to extract patterns of information flow directionality based on EEG phase dynamics. We quantify the complexity of these relative phase directionality patterns across various states using permutation entropy (PE) and statistical complexity measure within ordinal pattern framework. Our analysis: (i) PE is inversely correlated with the level of consciousness during general anesthesia, reflecting a dynamic interplay between anesthetic depth and shifts in directional information flow; (ii) healthy subjects consistently show higher PE than inADHD participants; (iii) when mapped onto the complexity-entropy causality plane, all brain states, regardless of condition or individual differences, align along a single curve, suggesting an underlying universal pattern in brain dynamics; and (iv) brain data consistently exhibit higher complexity than standard stochastic processes, likely due to greater multifractal scaling. These findings highlight that neural information propagation, as captured by EEG relative phase dynamics, is governed by self-organizing principles that are fundamentally more complex than the stochastic processes. Our EEG relative phase-based characterization provides new insight into the complexity of neural information flow directionality.
Data-driven Seasonal Climate Predictions via Variational Inference and Transformers
Authors: Lluís Palma, Alejandro Peraza, David Civantos, Amanda Duarte, Stefano Materia, Ángel G. Muñoz, Jesús Peña-Izquierdo, Laia Romero, Albert Soret, Markus G. Donat
Date: 2025-03-26 00:00:00
Tasks: Variational Inference

Most operational climate services providers base their seasonal predictions on initialised general circulation models (GCMs) or statistical techniques that fit past observations. GCMs require substantial computational resources, which limits their capacity. In contrast, statistical methods often lack robustness due to short historical records. Recent works propose machine learning methods trained on climate model output, leveraging larger sample sizes and simulated scenarios. Yet, many of these studies focus on prediction tasks that might be restricted in spatial extent or temporal coverage, opening a gap with existing operational predictions. Thus, the present study evaluates the effectiveness of a methodology that combines variational inference with transformer models to predict fields of seasonal anomalies. The predictions cover all four seasons and are initialised one month before the start of each season. The model was trained on climate model output from CMIP6 and tested using ERA5 reanalysis data. We analyse the method's performance in predicting interannual anomalies beyond the climate change-induced trend. We also test the proposed methodology in a regional context with a use case focused on Europe. While climate change trends dominate the skill of temperature predictions, the method presents additional skill over the climatological forecast in regions influenced by known teleconnections. We reach similar conclusions based on the validation of precipitation predictions. Despite underperforming SEAS5 in most tropics, our model offers added value in numerous extratropical inland regions. This work demonstrates the effectiveness of training generative models on climate model output for seasonal predictions, providing skilful predictions beyond the induced climate change trend at time scales and lead times relevant for user applications.
FuXi-RTM: A Physics-Guided Prediction Framework with Radiative Transfer Modeling
Authors: Qiusheng Huang, Xiaohui Zhong, Xu Fan, Lei Chen, Hao Li
Date: 2025-03-25 00:00:00
Tasks: Deep Learning, Video Generation, Weather Forecasting

Similar to conventional video generation, current deep learning-based weather prediction frameworks often lack explicit physical constraints, leading to unphysical outputs that limit their reliability for operational forecasting. Among various physical processes requiring proper representation, radiation plays a fundamental role as it drives Earth's weather and climate systems. However, accurate simulation of radiative transfer processes remains challenging for traditional numerical weather prediction (NWP) models due to their inherent complexity and high computational costs. Here, we propose FuXi-RTM, a hybrid physics-guided deep learning framework designed to enhance weather forecast accuracy while enforcing physical consistency. FuXi-RTM integrates a primary forecasting model (FuXi) with a fixed deep learning-based radiative transfer model (DLRTM) surrogate that efficiently replaces conventional radiation parameterization schemes. This represents the first deep learning-based weather forecasting framework to explicitly incorporate physical process modeling. Evaluated over a comprehensive 5-year dataset, FuXi-RTM outperforms its unconstrained counterpart in 88.51% of 3320 variable and lead time combinations, with improvements in radiative flux predictions. By incorporating additional physical processes, FuXi-RTM paves the way for next-generation weather forecasting systems that are both accurate and physically consistent.
Data-driven Mesoscale Weather Forecasting Combining Swin-Unet and Diffusion Models
Authors: Yuta Hirabayashi, Daisuke Matsuoka
Date: 2025-03-25 00:00:00
Tasks: Weather Forecasting

Data-driven weather prediction models exhibit promising performance and advance continuously. In particular, diffusion models represent fine-scale details without spatial smoothing, which is crucial for mesoscale predictions, such as heavy rainfall forecasting. However, the applications of diffusion models to mesoscale prediction remain limited. To address this gap, this study proposes an architecture that combines a diffusion model with Swin-Unet as a deterministic model, achieving mesoscale predictions while maintaining flexibility. The proposed architecture trains the two models independently, allowing the diffusion model to remain unchanged when the deterministic model is updated. Comparisons using the Fractions Skill Score and power spectral analysis demonstrate that incorporating the diffusion model leads to improved accuracy compared to predictions without it. These findings underscore the potential of the proposed architecture to enhance mesoscale predictions, particularly for strong rainfall events, while maintaining flexibility.
Information geometry of chemical reaction networks: Cramer-Rao bound and absolute sensitivity revisited
Authors: Dimitri Loutchko, Yuki Sughiyama, Tetsuya J. Kobayashi
Date: 2025-03-25 00:00:00
Tasks: Sensitivity

Information geometry is based on classical Legendre duality but allows to incorporate additional structure such as algebraic constraints and Bregman divergence functions. It is naturally suited, and has been successfully used, to describe the thermodynamics of chemical reaction networks (CRNs) based on the Legendre duality between concentration and potential spaces, where algebraic constraints are enforced by the stoichiometry. In this article, the Riemannian geometrical aspects of the theory are explored. It is shown that duality between concentration and potential spaces and the natural parametrizations of equilibrium subspace are isometries, which leads to a multivariate Cramer-Rao bound through the comparison of two Riemannian metric tensors. In the subsequent part, the theory is applied to the recently introduced concept of absolute sensitivity. Using the Riemannian geometric tools, it is proven that the absolute sensitivity is a projection operator onto the tangent bundle of the equilibrium manifold. A linear algebraic characterization and explicit results on first order corrections to the thermodynamics of ideal solutions are provided. Finally, the theory is applied to the IDHKP-IDH glyoxylate bypass regulation system. The novelty of the theory is that it is applicable to CRNs with non-ideal thermodynamical behavior, which are prevalent in highly crowded cellular environments due to various interactions between the chemicals. Indeed, the analyzed example shows remarkable behavior ranging from hypersensitivity to negative-self regulations. These are effects which usually require strongly nonlinear reaction kinetics. However, here, they are obtained by tuning thermodynamical interactions providing a complementary, and physically well-founded, viewpoint on such phenomena.
Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo
Authors: Zeno Schätzle, P. Bernát Szabó, Alice Cuzzocrea, Frank Noé
Date: 2025-03-25 00:00:00

The accurate quantum chemical calculation of excited states is a challenging task, often requiring computationally demanding methods. When entire ground and excited potential energy surfaces (PESs) are desired, e.g., to predict the interaction of light excitation and structural changes, one is often forced to use cheaper computational methods at the cost of reduced accuracy. Here we introduce a novel method for the geometrically transferable optimization of neural network wave functions that leverages weight sharing and dynamical ordering of electronic states. Our method enables the efficient prediction of ground and excited-state PESs and their intersections at the highest accuracy, demonstrating up to two orders of magnitude cost reduction compared to single-point calculations. We validate our approach on three challenging excited-state PESs, including ethylene, the carbon dimer, and the methylenimmonium cation, indicating that transferable deep-learning QMC can pave the way towards highly accurate simulation of excited-state dynamics.
Optimization through In-Context Learning and Iterative LLM Prompting for Nuclear Engineering Design Problems
Authors: M. Rizki Oktavian, Anirudh Tunga, Amandeep Bakshi, Michael J. Mueterthies, J. Thomas Gruenwald, Jonathan Nistor
Date: 2025-03-25 00:00:00
Tasks: In-Context Learning, Metaheuristic Optimization

The optimization of nuclear engineering designs, such as nuclear fuel assembly configurations, involves managing competing objectives like reactivity control and power distribution. This study explores the use of Optimization by Prompting, an iterative approach utilizing large language models (LLMs), to address these challenges. The method is straightforward to implement, requiring no hyperparameter tuning or complex mathematical formulations. Optimization problems can be described in plain English, with only an evaluator and a parsing script needed for execution. The in-context learning capabilities of LLMs enable them to understand problem nuances, therefore, they have the potential to surpass traditional metaheuristic optimization methods. This study demonstrates the application of LLMs as optimizers to Boiling Water Reactor (BWR) fuel lattice design, showing the capability of commercial LLMs to achieve superior optimization results compared to traditional methods.
Data-efficient rapid prediction of urban airflow and temperature fields for complex building geometries
Authors: Shaoxiang Qin, Dongxue Zhan, Ahmed Marey, Dingyang Geng, Theodore Potsis, Liangzhu Leon Wang
Date: 2025-03-25 00:00:00

Accurately predicting urban microclimate, including wind speed and temperature, based solely on building geometry requires capturing complex interactions between buildings and airflow, particularly long-range wake effects influenced by directional geometry. Traditional methods relying on computational fluid dynamics (CFD) are prohibitively expensive for large-scale simulations, while data-driven approaches struggle with limited training data and the need to model both local and far-field dependencies. In response, we propose a novel framework that leverages a multi-directional distance feature (MDDF) combined with localized training to achieve effective wind field predictions with minimal CFD data. By reducing the problem's dimensionality, localized training effectively increases the number of training samples, while MDDF encodes the surrounding geometric information to accurately model wake dynamics and flow redirection. Trained on only 24 CFD simulations, our localized Fourier neural operator (Local-FNO) model generates full 3D wind velocity and temperature predictions in under one minute, yielding a 500-fold speedup over conventional CFD methods. With mean absolute errors of 0.3 m/s for wind speed and 0.3 $^{\circ}$C for temperature on unseen urban configurations, our method demonstrates strong generalization capabilities and significant potential for practical urban applications.
Towards Long-Range ENSO Prediction with an Explainable Deep Learning Model
Authors: Qi Chen, Yinghao Cui, Guobin Hong, Karumuri Ashok, Yuchun Pu, Xiaogu Zheng, Xuanze Zhang, Wei Zhong, Peng Zhan, Zhonglei Wang
Date: 2025-03-25 00:00:00
Tasks: Deep Learning, Sensitivity

El Ni\~no-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with far-reaching global impacts. Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. In this study, we introduce CTEFNet, a multivariate deep learning model that synergizes convolutional neural networks and transformers to enhance ENSO forecasting. By integrating multiple oceanic and atmospheric predictors, CTEFNet extends the effective forecast lead time to 20 months while mitigating the impact of the spring predictability barrier, outperforming both dynamical models and state-of-the-art deep learning approaches. Furthermore, CTEFNet offers physically meaningful and statistically significant insights through gradient-based sensitivity analysis, revealing the key precursor signals that govern ENSO dynamics, which align with well-established theories and reveal new insights about inter-basin interactions among the Pacific, Atlantic, and Indian Oceans. The CTEFNet's superior predictive skill and interpretable sensitivity assessments underscore its potential for advancing climate prediction. Our findings highlight the importance of multivariate coupling in ENSO evolution and demonstrate the promise of deep learning in capturing complex climate dynamics with enhanced interpretability.
Federated Learning: A new frontier in the exploration of multi-institutional medical imaging data
Authors: Dominika Ciupek, Maciej Malawski, Tomasz Pieciak
Date: 2025-03-25 00:00:00
Tasks: Data Integration, Federated Learning

Artificial intelligence has transformed the perspective of medical imaging, leading to a genuine technological revolution in modern computer-assisted healthcare systems. However, ubiquitously featured deep learning (DL) systems require access to a considerable amount of data, facilitating proper knowledge extraction and generalization. Admission to such extensive resources may be hindered due to the time and effort required to convey ethical agreements, set up and carry the acquisition procedures through, and manage the datasets adequately with a particular emphasis on proper anonymization. One of the pivotal challenges in the DL field is data integration from various sources acquired using different hardware vendors, diverse acquisition protocols, experimental setups, and even inter-operator variabilities. In this paper, we review the federated learning (FL) concept that fosters the integration of large-scale heterogeneous datasets from multiple institutions in training DL models. In contrast to a centralized approach, the decentralized FL procedure promotes training DL models while preserving data privacy at each institution involved. We formulate the FL principle and comprehensively review general and dedicated medical imaging aggregation and learning algorithms, enabling the generation of a globally generalized model. We meticulously go through the challenges in constructing FL-based systems, such as data heterogeneity across the institutions, resilience to potential attacks on data privacy, and the variability in computational and communication resources among the entangled sites that might induce efficiency issues of the entire system. Finally, we explore the up-to-date open frameworks for rapid FL-based algorithm prototyping and shed light on future directions in this intensively growing field.