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simulation-based inference of surface accumulation and basal melt rates of an Antarctic ice shelf from isochronal layers

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JOURNAL OF GLACIOLOGY 2025年 71卷 e44-e44页

作者： Moss, Guy Visnjevic, Vjeran Eisen, Olaf Oraschewski, Falk M. Schroeder, Cornelius Macke, Jakob H. Drews, Reinhard Univ Tubingen Machine Learning Sci Tubingen Germany Tubingen AI Ctr Tubingen Germany Univ Tubingen Dept Geosci Tubingen Germany Helmholtz Zentrum Polar & Meeresforsch Alfred Wegener Inst Bremerhaven Germany Univ Bremen Fac Geosci Bremen Germany Max Planck Inst Intelligent Syst Tubingen Germany

The ice shelves buttressing the Antarctic ice sheet determine the rate of ice-discharge into the surrounding oceans. Their geometry and buttressing strength are influenced by the local surface accumulation and basal melt rates, governed by atmospheric and oceanic conditions. Contemporary methods quantify one of these rates, but typically not both. Moreover, information about these rates is only available for recent time periods, reaching at most a few decades back since measurements are available. We present a new method to simultaneously infer the surface accumulation and basal melt rates averaged over decadal and centennial timescales. We infer the spatial dependence of these rates along flow line transects using internal stratigraphy observed by radars, using a kinematic forward model of internal stratigraphy. We solve the inverse problem using simulation-based inference (SBI). SBI performs Bayesian inference by training neural networks on simulations of the forward model to approximate the posterior distribution, therefore also quantifying uncertainties over the inferred parameters. We validate our method on a synthetic example, and apply it to Ekstr & ouml;m Ice Shelf, Antarctica, for which independent validation data are available. We obtain posterior distributions of surface accumulation and basal melt averaging over up to 200 years before 2022.

关键词： Bayesian inference Ice shelves Melt - basal Mass-balance reconstruction Machine learning simulation-based inference

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Role of denoisers in simulation-based inference from graph-structured data: a case study for position inference in an astroparticle detector

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2025年第1期2025卷 1-24页

作者： Roy, Venkat Higuera, Aaron Liang, Shixiao Peters, Christina Bajwa, Waheed U. Tunnell, Christopher D. Rutgers Univ New Brunswick Dept Elect & Comp Engn New Brunswick NJ 08816 USA Rice Univ Dept Phys & Astron Houston TX USA Univ Delaware Dept Comp & Informat Sci Newark DE USA Rutgers Univ New Brunswick Dept Stat New Brunswick NJ 08901 USA Rice Univ Dept Comp Sci Houston TX USA

The focus of this paper is to improve simulation-based inference through improved denoising of experimental data. Statistical inference of parameters governing a complex physical process from observed data is an important task for several scientific domains. In a likelihood-free inference setup, the inference engine used to make inference from the experimental data can be trained using simulated data. In many scenarios, experimental data is corrupted by noise during the data acquisition process, which is either unaccounted for in the simulator or whose strength might not match with the one set in the simulator. This deteriorates the accuracy of the inference. While advances in denoising can be leveraged to address this challenge, many denoising techniques ignore the fact that experimental data in many scientific domains tends to be irregular/graph-structured. This paper addresses these two challenges by developing a kernel-based learnable graph (KBLG) denoiser, which can be used to denoise experimental graph-structured data. In order to exhibit the efficacy of the developed denoiser in simulation-driven inference problems, this paper considers the problem of inference of the position of an interaction in an astroparticle detector. The available simulated data in this case is the snapshots of the luminous responses of the photomultiplier tube sensors used within the dark matter detection experiment. In experimental situations, these measurements are corrupted by noise generated by secondary optical and electronic processes. The proposed KBLG denoiser is used to denoise the multiple snapshots of the experimental measurements, which are then used for position reconstruction using a multilayer perceptron trained using noiseless simulated data. Numerical results exhibit that the proposed KBLG denoiser outperforms a graph-agnostic denoiser in terms of MLP-based position reconstruction performances for different levels of noise (noise variance).

关键词： Astroparticle detector Graph learning Kernel trick Multilayer perceptron simulation-based inference

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Comparing AI versus optimization workflows for simulation-based inference of spatial-stochastic systems

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2025年第1期6卷

作者： Sierra, Michael Alexander Ramirez Sokolowski, Thomas R. Frankfurt Inst Adv Studies FIAS Ruth Moufang Str 1 D-60438 Frankfurt Germany Goethe Univ Frankfurt am Main Fac Comp Sci & Math Robert Mayer Str 10 D-60054 Frankfurt Am Main Germany

Model parameter inference is a universal problem across science. This challenge is particularly pronounced in developmental biology, where faithful mechanistic descriptions require spatial-stochastic models with numerous parameters, yet quantitative empirical data often lack sufficient granularity due to experimental limitations. Parameterizing such complex models therefore necessitates methods that elaborate on classical Bayesian inference by incorporating notions of optimality and goal-orientation through low-dimensional objective functions that quantitatively encapsulate target system behavior. In this study, we contrast two such inference workflows and apply them to biophysically inspired spatial-stochastic models. Technically, both workflows employ simulation-based inference (SBI) methods: the first leverages a modern deep-learning technique known as sequential neural posterior estimation, while the second relies on a classical optimization technique called simulated annealing. We evaluate these workflows by inferring the parameters of two complementary models for the inner cell mass (ICM) lineage differentiation in the blastocyst-stage mouse embryo. This developmental biology system serves as a paradigmatic example of a highly robust and reproducible cell-fate proportioning process that self-organizes under strongly stochastic conditions, such as intrinsic biochemical noise and cell-cell signaling delays. Our results reveal that while both methods provide consistent model parameter estimates, the modern SBI workflow yields significantly richer inferred distributions at an equivalent computational cost. We identify the computational scenarios that favor the modern SBI method over its classical counterpart, and propose a plausible strategy to exploit the complementary strengths of both workflows for enhanced parameter space exploration.

关键词： simulation-based inference AI optimization spatial-stochastic modeling quantitative biology gene regulatory networks cell-cell signaling

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simulation-based inference of single-molecule experiments

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CURRENT OPINION IN STRUCTURAL BIOLOGY 2025年 91卷 102988页

作者： Dingeldein, Lars Cossio, Pilar Covino, Roberto Goethe Univ Frankfurt Inst Phys Frankfurt Germany Frankfurt Inst Adv Studies Frankfurt Germany Flatiron Inst Ctr Computat Math New York NY USA Flatiron Inst Ctr Computat Biol New York NY USA Goethe Univ Frankfurt Inst Comp Sci Frankfurt Germany

Single-molecule experiments are a unique tool to characterize the structural dynamics of biomolecules. However, reconstructing molecular details from noisy single-molecule data is challenging. simulation-based inference (SBI) is a powerful framework for analyzing complex experimental data, integrating statistical inference, physics-based simulators, and machine learning. Recent advances in deep learning have accelerated the development of new SBI methods, enabling the application of Bayesian inference to an ever-increasing number of scientific problems. Here, we review the nascent application of SBI to the analysis of single-molecule experiments. We introduce parametric Bayesian inference and discuss its limitations. We then overview emerging deep learning-based SBI methods to perform Bayesian inference for complex models encoded in computer simulators. We illustrate the first applications of SBI to single-molecule force spectroscopy and cr yo-electron microscopy experiments. SBI allows us to leverage powerful computer algorithms modeling complex biomolecular phenomena to connect scientific models and experiments in a principled way.

关键词： simulation-based inference Likelihood-free inference Single-molecule data analysis Bayesian inference Cryo-electron microscopy

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simulation-based inference for reliability model selection and parameter calibration: An application to fatigue

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第4期40卷 1855-1874页

作者： Ben Abdessalem, Anis Univ Angers LARIS SFR MATHSTIC F-49000 Angers France

Selecting a "useful" model among a set of competing candidate models is a common problem in many disciplines and particularly in engineering applications. For a modeller, the best model fulfills a certain purpose best (e.g., the ability to accurately predict new data, good interpolation, precise estimates of lower/upper percentiles, etc.), which is typically assessed by comparing model simulations to the observed data. Approximate Bayesian computation (ABC) which is a statistical learning technique is used in this work for reliability model selection and parameter calibration using small/moderate fatigue life data sets. This is always the case in material fatigue due to the high cost and low efficiency of fatigue tests which are bottleneck problem when designing components/structures with regard to fatigue. The ABC is a likelihood-free based method which means that it needs only a generative model to simulate the data. The proposed method provides a formal rank of the competing reliability models by eliminating gradually the least likely models in a parsimonious manner. It is flexible since it offers the possibility to use different metrics to measure the discrepancy between simulated and observed data. The choice of the appropriate distance function depends essentially on the purpose of model selection. Through various examples, it has been demonstrated that the ABC method has a number of very attractive properties, which makes it especially useful in probabilistic risk assessment and reliability analysis.

关键词： approximate Bayesian computation fatigue life distributions low percentiles probabilistic fatigue design reliability model selection risk assessment simulation-based inference

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simulation-based inference in dynamic panel probit models: An application to health

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Empirical Economics 2004年第1期29卷 49-77页

作者： Contoyannis, Paul Jones, Andrew M. Rice, Nigel Ctr. for Hlth. Econ./Policy Analysis McMaster University Hamilton Ont. L8N 3Z5 1200 Main Street West Canada Dept. of Economics/Related Studies University of York York YO10 5DD United Kingdom Centre for Health Economics University of York Heslington York YO10 5DD United Kingdom

This paper considers the determinants of a binary indicator for the existence of functional limitations using seven waves (1991-1997) of the British Household Panel Survey (BHPS). The focal point of our analysis is the contributions of state dependence, heterogeneity and serial correlation in explaining the dynamics of health. To investigate these issues we apply static and dynamic panel probit models with flexible error structures. To estimate the models we use maximum simulated likelihood (MSL) with antithetic acceleration and implement a recently proposed test for the existence of asymptotic bias. The dynamic models show strong positive state dependence.

关键词： Binary choice panel data models Health dynamics simulation-based inference

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Lightweight starshade position sensing with convolutional neural networks and simulation-based inference

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JOURNAL OF ASTRONOMICAL TELESCOPES INSTRUMENTS AND SYSTEMS 2023年第2期9卷

作者： Chen, Andrew Harness, Anthony Melchior, Peter Princeton Univ Dept Comp Sci Princeton NJ USA Princeton Univ Dept Mech & Aerosp Engn Princeton NJ USA Princeton Univ Dept Astrophys Sci Princeton NJ 08544 USA Princeton Univ Ctr Stat & Machine Learning Princeton NJ 08544 USA

Starshades are a leading technology to enable the direct detection and spectroscopic characterization of Earth-like exoplanets. To maintain high contrast during observations, the starshade and telescope must keep within 1 m of relative alignment over large separations (>20;000 km). This formation flying is made possible with precise spacecraft position information obtained through accurate sensing of the occulted star's diffraction peak (referred to as the spot of Arago) incident on the telescope aperture. We present a lightweight image processing method based on a convolutional neural network paired with a simulation-based inference technique to estimate the position of the spot of Arago and its uncertainty. On simulated images, the method achieves an accuracy of a few centimeters across the entire telescope aperture. By deploying our method at the Princeton Starshade Testbed, we demonstrate that the neural network can be trained on simulated images and used on real images and that it can successfully be integrated in the control system for closed-loop formation flying. (c) 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： starshade convolutional neural network simulation-based inference formation flying

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Improving Effectiveness of simulation-based inference in the Massively Parallel Regime

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第4期34卷 1100-1114页

作者： Kulkarni, Sourabh Moritz, Csaba Andras Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01003 USA

simulation-based inference (SBI) is a widely used set of algorithms to learn the parameters of complex scientific simulation models. While primarily run on CPUs in High-Performance Compute clusters, these algorithms have been shown to scale in performance when developed to be run on massively parallel architectures such as GPUs. While parallelizing existing SBI algorithms provides us with performance gains, this might not be the most efficient way to utilize the achieved parallelism. This work proposes a new parallelism-aware adaptation of an existing SBI method, namely approximate Bayesian computation with Sequential Monte Carlo(ABC-SMC). This new adaptation is designed to utilize the parallelism not only for performance gain, but also toward qualitative benefits in the learnt parameters. The key idea is to replace the notion of a single 'step-size' hyperparameter, which governs how the state space of parameters is explored during learning, with step-sizes sampled from a tuned Beta distribution. This allows this new ABC-SMC algorithm to more efficiently explore the state-space of the parameters being learned. We test the effectiveness of the proposed algorithm to learn parameters for an epidemiology model running on a Tesla T4 GPU. Compared to the parallelized state-of-the-art SBI algorithm, we get similar quality results in similar to 100x fewer simulations and observe similar to 80x lower run-to-run variance across 10 independent trials.

关键词： inference algorithms Computational modeling Biological system modeling Approximation algorithms Perturbation methods Optimization Bayes methods Approximate Bayesian computation Bayesian inference COVID-19 compartmental models epidemiology GPU high performance computing likelihood-free inference parallel algorithms simulation-based inference statistical machine learning stochastic optimization

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simulation-based inference with approximately correct parameters via maximum entropy

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2022年第2期3卷 025006页

作者： Barrett, Rainier Ansari, Mehrad Ghoshal, Gourab White, Andrew D. Univ Rochester Dept Chem Engn Rochester NY 14627 USA Univ Rochester Dept Phys & Astron Rochester NY 14627 USA Univ Rochester Dept Comp Sci Rochester NY 14627 USA

Inferring the input parameters of simulators from observations is a crucial challenge with applications from epidemiology to molecular dynamics. Here we show a simple approach in the regime of sparse data and approximately correct models, which is common when trying to use an existing model to infer latent variables with observed data. This approach is based on the principle of maximum entropy (MaxEnt) and provably makes the smallest change in the latent joint distribution to fit new data. This method requires no likelihood or model derivatives and its fit is insensitive to prior strength, removing the need to balance observed data fit with prior belief. The method requires the ansatz that data is fit in expectation, which is true in some settings and may be reasonable in all settings with few data points. The method is based on sample reweighting, so its asymptotic run time is independent of prior distribution dimension. We demonstrate this MaxEnt approach and compare with other likelihood-free inference methods across three systems: a point particle moving in a gravitational field, a compartmental model of epidemic spread and molecular dynamics simulation of a protein.

关键词： simulation-based inference maximum entropy likelihood-free derivative-free

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simulation-based inference on virtual brain models of disorders

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第3期5卷 035019页

作者： Hashemi, Meysam Ziaeemehr, Abolfazl Woodman, Marmaduke M. Fousek, Jan Petkoski, Spase Jirsa, Viktor K. Inst Neurosci Syst Aix Marseille Univ INSERM INS Marseille France

Connectome-based models, also known as virtual brain models (VBMs), have been well established in network neuroscience to investigate pathophysiological causes underlying a large range of brain diseases. The integration of an individual's brain imaging data in VBMs has improved patient-specific predictivity, although Bayesian estimation of spatially distributed parameters remains challenging even with state-of-the-art Monte Carlo sampling. VBMs imply latent nonlinear state space models driven by noise and network input, necessitating advanced probabilistic machine learning techniques for widely applicable Bayesian estimation. Here we present simulation-based inference on VBMs (SBI-VBMs), and demonstrate that training deep neural networks on both spatio-temporal and functional features allows for accurate estimation of generative parameters in brain disorders. The systematic use of brain stimulation provides an effective remedy for the non-identifiability issue in estimating the degradation limited to smaller subset of connections. By prioritizing model structure over data, we show that the hierarchical structure in SBI-VBMs renders the inference more effective, precise and biologically plausible. This approach could broadly advance precision medicine by enabling fast and reliable prediction of patient-specific brain disorders.

关键词： virtual brain models simulation-based inference degradation structural and functional connectivity non-identifiability stimulation hierarchical structure

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