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Machine Learning: Science and Technology
Scopus
ISSN 2632-2153 · IOP Publishing · 1,144 articles in the index
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Journal Article
Error estimates for a physics-informed neural network in solving KdV equations
This paper aims to provide error bounds on physics-informed neural network (PINN) in solving Korteweg–de Vries (KdV) equations. We prove that a neural network equipped with two hidden layers and the tanh activation function can reduce the partial differential equation residuals arbitrarily. The…
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Journal Article
Quantum generative modeling for financial time series with temporal correlations
Quantum generative adversarial networks (QGANs) have been investigated as a method for generating synthetic data with the goal of augmenting training data sets for neural networks. This is especially relevant for financial time series, since we only ever observe one realization of the process…
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Journal Article
Interpretable feature interaction via statistical self-supervised learning on tabular data
In high-stakes scientific contexts, explainable AI is crucial for deriving meaningful insights from complex tabular data. A formidable challenge is ensuring both rigorous statistical guarantees and clear interpretability in feature extraction. While traditional methods like principal component…
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Journal Article
Neural importance resampling: a practical sampling strategy for neural quantum states
Neural quantum states (NQS) have emerged as powerful tools for simulating many-body quantum systems, but their practical use is often hindered by limitations of current sampling techniques. Markov chain Monte Carlo (MCMC) methods suffer from slow mixing and require manual tuning, while…
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Journal Article
Uncertainty quantification of surrogate models using conformal prediction
Data-driven surrogate models offer fast, inexpensive approximations to complex numerical and experimental systems but typically lack uncertainty quantification, limiting their reliability in safety-critical applications. While Bayesian methods provide uncertainty estimates, they offer no…
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Journal Article
Machine learning-driven classification of natural disasters via parallel confidence fusion
The low-frequency characteristics of infrasound signals enable them to play a critical role in the long-range detection of disasters such as earthquakes, landslides, and chemical explosions. However, the accurate classification of disaster types based on infrasound signals remains a significant…
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Journal Article
Constrained collaborative optimization of charged particle tracking with multi-agent reinforcement learning
Reinforcement learning (RL) demonstrated immense success in modeling complex physics-driven systems, providing end-to-end trainable solutions by interacting with a simulated or real environment, maximizing a scalar reward signal. In this work, we propose, building upon previous work, an end-to-end…
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Journal Article
Simultaneous approximation of multiple degenerate states using a single neural network quantum state
Neural network quantum states (NQSs) excel at approximating ground states of quantum many-body systems, but approximating all states of a degenerate manifold is nevertheless computationally expensive. We propose a single-trunk multi-head (ST-MH) NQS ensemble that shares a feature-extracting trunk…
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Journal Article
Data-driven and self-supervised spectral operator learning methods for heat conduction equation with variable source functions
Operator learning methodologies have gained significant attention as a powerful machine learning paradigm for solving partial differential equations (PDEs), yet their reliance on large labeled datasets for training limits broad applicability. This work advances the spectral operator learning (SOL)…
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Journal Article
Towards foundation models for experimental readout systems combining discrete and continuous data
We present a (proto) foundation model for nuclear physics, capable of operating on low-level detector inputs from Imaging Cherenkov Detectors at the future electron ion collider. Building upon established next-token prediction approaches, we aim to address potential challenges such as resolution…
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Journal Article
Particle-resolved turbulence detection in complex plasmas using LSTM neural network
Turbulence plays an important role in fluids and plasmas, but its detection at the level of individual particles remains challenging. In this work, we introduce a long short-term memory (LSTM) neural network for the classification of turbulence in complex plasmas based on particle-resolved…
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Journal Article
Trustworthy AI-based crack-tip segmentation using domain-guided explanations
Ensuring the trustworthiness and robustness of deep learning models remains a fundamental challenge, particularly in high-stakes scientific applications. In this study, we present a framework called attention-guided training that combines explainable artificial intelligence techniques with…
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Journal Article
GAP vs. MACE: efficiency evaluation in a liquid electrolyte system
Machine learning interatomic potentials (MLIPs) have revolutionized molecular simulations, but as they evolve, so does the demand for advanced computing architectures, particularly graphics processing units (GPUs). However, the high cost of GPUs limits accessibility, making it crucial to compare…
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Journal Article
Quantum vs. classical: a comprehensive benchmark study for predicting time series with variational quantum machine learning
Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectively than classical approaches. However, their practical advantage over established classical methods remains uncertain…
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Journal Article
U-TraCE: a conformal prediction approach to uncertainty quantification in black-box models
Modern machine learning (ML) systems, particularly deep neural networks, have become increasingly complex and opaque, making reliable uncertainty quantification (UQ) essential. Conventional UQ methods often prove inadequate for black-box model evaluation scenarios, either because model internals…
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Journal Article
Patch-MLP-based predictive control: simulation of upstream pointing stabilization for PHELIX laser system
High-energy laser facilities such as PHELIX at GSI require excellent beam pointing stability for reproducibility and relative independence for future experiments. Beam pointing stability has been traditionally achieved using simple proportional-integral-derivative (PID) control which removes the…
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Journal Article
Machine-learning-based inverse design of wide-band metasurfaces with interdependent unit cells
Metasurfaces are thin optical materials consisting of tiny meta-atoms that locally change the phase, amplitude, and polarization of light. Such metasurfaces have been shown to exhibit optical responses beyond what is possible with natural materials, e.g. in thin achromatic lenses, holograms…
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Journal Article
Design, assessment, and application of machine learning potential energy surfaces
Potential energy surfaces (PES) are an indispensable tool in the investigation, characterization and understanding of chemical and biological systems in the gas and condensed phases. Advances in machine learning (ML) methodologies have led to the development of ML-PES, which are now widely used to…
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Journal Article
Leveraging support vector regression, radiomics and dosiomics for outcome prediction in personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR)
Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a novel treatment that delivers radiation in pulses of protracted intervals. Accurate prediction of gross tumor volume (GTV) changes through regression models has substantial prognostic value. This study aims to develop…
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Journal Article
Train stochastic non linear coupled ODEs to classify and generate
A general class of dynamical systems which can be trained to operate in classification and generation modes are introduced. A procedure is proposed to plant asymptotic stationary attractors of the deterministic model. Optimizing the dynamical system amounts to shaping the architecture of…
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