Paper presents a deep learning framework for faster, more accurate nuclear reactor power prediction.
problem Inaccurate and inefficient modeling of nuclear reactor transients.
method Hybrid digital twin-focused multi-stage deep learning framework using feed-forward neural networks.
result Achieved remarkable accuracy (96% classification, 2.3% MAPE) with noise-enhanced simulated data.
Paper uses autoencoders for efficient reduced-order modeling of eigenvalue problems.
problem Efficiently modeling eigenvalue problems in high dimensions.
method Autoencoder-based reduced-order modeling for eigenvalue problems.
result Autoencoder-based models outperform standard POD-Galerkin methods in neutron diffusion applications.
Paper tackles robust prediction of nuclear reactor materials under scarce data.
problem Challenges of data scarcity and uncertainty in nuclear reactor design.
method Meta-learning approach informed by uncertainty and prior knowledge.
result Achieves superior performance in rupture life prediction.
This paper applies reactor theory to supply chain management.
problem Maintaining optimal item delivery and collection ratios in supply chains.
method Translating neutron transport and diffusion theory to supply chain management, introducing analogy factors and interactors.
result A deterministic model for supply chain optimization.
Nuclear fusion is regarded as the energy of the future since it presents the possibility of unlimited clean energy. One obstacle in utilizing fusion as a feasible energy source is the stability of the reaction. Ideally, one would have a controller for the reactor that makes actions in response to the current state of t…
Neural surrogates speed up 5D gyrokinetic simulations of plasma turbulence.
problem Expensive numerical simulations of plasma turbulence hinder fusion reactor design.
method Trained a hierarchical vision transformer in 5D to predict plasma quantities faster.
result Neural surrogates predict plasma quantities two orders of magnitude faster than numerical codes.
ANNs predict SAFARI-1 neutron fluxes with uncertainties.
problem Uncertainty quantification in ANN predictions for SAFARI-1.
method Deep Neural Networks (DNNs) with Monte Carlo Dropout (MCD) and Bayesian Neural Networks (BNN VI) for uncertainty quantification.
result Uncertainty bands envelop noisy measurement data points, indicating good prediction and generalization.
In this paper, we take the first steps towards a novel unified framework for the analysis of perturbations in both the Time and Frequency domains. The identification of type and source of such perturbations is fundamental for monitoring reactor cores and guarantee safety while running at nominal conditions. A 3D Convol…
GyroSwin models plasma turbulence with neural nets, reducing costs and capturing neglected nonlinearities.
problem Understanding plasma turbulence in fusion reactors, which impairs confinement and limits reactor design.
method Introduces GyroSwin, a scalable 5D neural surrogate that approximates 5D nonlinear gyrokinetic simulations.
result GyroSwin outperforms reduced models in heat flux prediction and captures turbulent energy cascade.
NDS learns dynamical models with prior knowledge, improving accuracy and efficiency.
problem Learning accurate dynamical models with limited data and varying dynamics.
method Neural Dynamical Systems (NDS) integrates prior knowledge in ODEs with neural networks to estimate parameters and predict states.
result NDS achieves higher accuracy and uses fewer samples compared to other methods.
In the first section, this report analyses Nuclear Power Plants (NPPs) in the context of megaprojects, explaining why they are often delivered over budget and late. In the second section, the report discusses how Small Modular Reactors (SMRs) might address these issues. Megaprojects are extremely risky and often implem…
Study on AI-driven modeling for high burnup accident-tolerant fuels in SMRs.
problem Design and optimization of high burnup accident-tolerant fuels for SMRs.
method Artificial intelligence and multi-scale modeling (neutronics, thermal hydraulics, fuel performance).
result Demonstrated the effectiveness of AI in modeling and optimizing SMR fuels.
Study uses deep reinforcement learning for real-time control of nuclear microreactors, achieving similar or superior performance to traditional PID controllers.
problem Minimizing operating costs of nuclear microreactors through autonomous control, especially in load-following scenarios.
method Application of deep reinforcement learning (RL) for real-time drum control in microreactors, using point kinetics model with thermal and xenon feedback.
result Deep reinforcement learning controllers, including single- and multi-agent RL frameworks, can achieve similar or superior load-following performance to traditional PID control across various scenarios.
Estimates reliability of nuclear fuel using advanced modeling techniques.
problem Determining the reliability of TRISO-coated particle fuel, which has small failure probabilities and expensive computational models.
method Coupled active learning, multifidelity modeling, and subset simulation.
result Multifidelity modeling strategies consistently reduce the number of high-fidelity model calls.
We compare correlations and coherent structures in nuclei and financial markets. In the nuclear physics part we review giant resonances which can be interpreted as a coherent structure embedded in chaos. With similar methods we investigate the financial empirical correlation matrix of the DAX and Dow Jones. We will sho…
We implement machine learning algorithms to nuclear data. These algorithms are purely data driven and generate models that are capable to capture intricate trends. Gradient boosted trees algorithm is employed to generate a trained model from existing nuclear data, which is used for prediction for data of damping parame…
Theoretical models of the strong nuclear interaction contain unknown coupling constants (parameters) that must be determined using a pool of calibration data. In cases where the models are complex, leading to time consuming calculations, it is particularly challenging to systematically search the corresponding paramete…
This paper emphasizes the need for uncertainty quantification in data-driven ML models for nuclear engineering.
problem Uncertainty in ML predictions due to data noise, model architecture, and stochastic training.
method Explains and compares uncertainties in physics-based and data-driven models, and presents techniques to quantify ML prediction uncertainties.
result The importance of uncertainty quantification in ML models for nuclear engineering applications.
Efficiently calibrates computationally expensive models using vine copulas.
problem Computational models are expensive and hard to calibrate with real data.
method Variational Bayes inference with vine copulas for dependent data.
result Computational scalability and efficiency of the proposed algorithm.
Improved nuclear cross section fitting with weighted Levenberg-Marquardt method.
problem Challenging optimization in multichannel nuclear cross section data.
method Weighted Levenberg-Marquardt algorithm with Fisher Information Metric.
result More physically consistent fits for raw and smoothed datasets.
Deep learning compares turbulence models in plasma physics.
problem Predicting edge plasma turbulence in magnetic fusion reactors.
method Physics-informed deep learning framework for comparing two-fluid and gyrokinetic models.
result Good overall agreement between two-fluid theory and gyrokinetic models in turbulent field fluctuations.
AI advances impact asset management, offering new decision-making capabilities.
problem AI's impact on asset management and potential disruption.
method Analyzing how AI capabilities (reading, reasoning, decision-making) can be applied to asset management.
result AI can revolutionize asset management, but risks vary by fund type.
The region of heavy calcium isotopes forms the frontier of experimental and theoretical nuclear structure research where the basic concepts of nuclear physics are put to stringent test. The recent discovery of the extremely neutron-rich nuclei around 60Ca [Tarasov, 2018] and the experimental determination of masse…
Safe offline RL for chemical reactors using input convex neural networks.
problem Safe control of exothermic polymerization reactors using historical data.
method Gymnasium-compatible simulation, behaviour cloning, implicit Q-learning, input convex neural networks (PICNNs).
result Offline RL with convex action correction outperforms traditional control approaches.
Formula found for knot determinant in 3-braid weaving.
problem Determining the determinant of a specific type of knot.
method Developed a formula for the determinant of the twisted generalized hybrid weaving knot.
result Proved a conjecture about the knot's determinant.
In this paper, we show the implementation of deep neural networks applied in process control. In our approach, we based the training of the neural network on model predictive control. Model predictive control is popular for its ability to be tuned by the weighting matrices and by the fact that it respects the constrain…
New ML methods improve physical system understanding by quantifying uncertainty across diverse regimes.
problem Capturing multi-regime physical systems with standard ML techniques.
method Coverage-oriented uncertainty quantification (UQ) methods.
result Coverage-oriented UQ models deliver physically consistent uncertainty estimates.
New method unfolds distribution moments directly from data without binning.
problem Deconvolving detector distortions in particle physics.
method Uses machine learning, inspired by GANs, to unfold moments directly.
result More precise than bin-based approaches and comparable to unbinned methods.
Unified access package for fundamental physics datasets simplifies machine learning.
problem Lack of unified access to datasets from multiple fundamental physics disciplines.
method Unified Python package with common interface and reference models.
result Graph-based neural networks perform similarly to dedicated methods on various datasets.
NukeBERT improves performance on nuclear domain Q&A with less training data.
problem Lack of annotated data for nuclear domain Q&A.
method Developed NQuAD dataset and NukeBERT model incorporating novel BERT vocabulary technique.
result NukeBERT outperformed BERT significantly on NQuAD.
BODE enhances deep neural network predictions and uncertainty quantification in safety modeling.
problem Uncertainty in deep neural network predictions for safety-critical applications.
method Bayesian optimization combined with deep ensembles (BODE).
result BODE reduces total uncertainty by over 30% compared to a manually tuned baseline ensemble.
Study on tensor nuclear norm's decomposability and subdifferential.
problem Understanding tensor nuclear norm in higher-order tensors.
method Showed decomposability over specific subspaces, derived subdifferential inclusions, and studied subgradients.
result Established the statistical performance of tensor robust principal component analysis.
We consider an open domain with a compact boundary in an Euclidean space and a Schroedinger operator with magnetic field on this domain. We give sufficient conditions on the rate of growth of the magnetic field near the boundary which guarantees essential self-adjointness of this operator. From the physical point of vi…
Efficiently regularizes deep learning models using Jacobian nuclear norm.
problem Regularizing deep learning models to prevent overfitting and improve generalization.
method Proposes a denoising-style approximation to penalize the Jacobian nuclear norm without computing the Jacobian matrix.
result Demonstrates that penalizing the average squared Frobenius norm of Jg and Jh is equivalent to penalizing the Jacobian nuclear norm for function compositions. Adapts Gaussian process surrogate evaluation with conformal prediction for better coverage guarantees.
problem Uncertainty quantification and model specification issues in Gaussian process surrogate models.
method Adaptive cross-conformal prediction intervals using posterior standard deviation weighting.
result Conformal prediction intervals provide significant correlation with surrogate model error and frequentist coverage guarantees.
The limits of the nuclear landscape are determined by nuclear binding energies. Beyond the proton drip lines, where the separation energy becomes negative, there is not enough binding energy to prevent protons from escaping the nucleus. Predicting properties of unstable nuclear states in the vast territory of proton em…
Kolmogorov-Arnold Networks offer interpretable models for energy applications.
problem Lack of interpretability in modern machine learning methods for sensitive industries.
method Symbolic regression with Kolmogorov-Arnold Networks compared to traditional feedforward neural networks.
result Kolmogorov-Arnold Networks yield perfectly interpretable models and learn real, physical relations.
Low-rank matrix recovery has found many applications in science and engineering such as machine learning, signal processing, collaborative filtering, system identification, and Euclidean embedding. But the low-rank matrix recovery problem is an NP hard problem and thus challenging. A commonly used heuristic approach is…
Minimizing the nuclear norm of a matrix has been shown to be very efficient in reconstructing a low-rank sampled matrix. Furthermore, minimizing the sum of nuclear norms of matricizations of a tensor has been shown to be very efficient in recovering a low-Tucker-rank sampled tensor. In this paper, we propose to recover…
Deep learning predicts nuclear equation of state from rotating core collapse GW signals.
problem Classifying the nuclear equation of state from rotating core collapse gravitational wave signals.
method Employed deep convolutional neural networks to classify visual and temporal patterns in GW signals.
result Up to 97% correct classifications of nuclear equation of state in the test set.
Quantum computing improves fault diagnosis in industrial processes.
problem Fault detection and diagnosis in industrial process systems.
method Integrates quantum computing and deep learning to extract features and diagnose faults.
result Quantum-assisted deep learning achieves high fault detection rates (79.2% and 99.39%).
DeepONet accelerates nuclear DT inference with high accuracy and efficiency.
problem Real-time prediction and model evaluation in nuclear systems.
method Deep Neural Operator (DeepONet) for surrogate modeling.
result DeepONet outperforms traditional ML methods in accuracy and speed.
Nuclear segmentation in histology images is a challenging task due to significant variations in the shape and appearance of nuclei. One of the main hurdles in nuclear instance segmentation is overlapping nuclei where a smart algorithm is needed to separate each nucleus. In this paper, we introduce a proposal-free deep …
The chart of the nuclides is limited by particle drip lines beyond which nuclear stability to proton or neutron emission is lost. Predicting the range of particle-bound isotopes poses an appreciable challenge for nuclear theory as it involves extreme extrapolations of nuclear masses beyond the regions where experimenta…
Deep learning detects corrosion in nuclear fuel canisters.
problem Ensuring safety and integrity of used nuclear fuel dry storage canisters.
method Residual neural networks (ResNets) for real-time corrosion detection of canister images.
result Deep learning approach accurately detects corrosion and classifies canisters as corroded or intact.
New method optimizes expensive simulations for complex systems.
problem Optimizing complex systems with limited expensive experiments.
method Black-box Optimization via Marginal Means (BOMM) approach.
result BOMM improves optimization performance in high dimensions.
Recently theoretical guarantees have been obtained for matrix completion in the non-uniform sampling regime. In particular, if the sampling distribution aligns with the underlying matrix's leverage scores, then with high probability nuclear norm minimization will exactly recover the low rank matrix. In this article, we…
In the area of traditional physics the atomic nucleus belongs to the most complex systems. It involves essentially all elements that characterize complexity including the most distinctive one whose essence is a permanent coexistence of coherent patterns and of randomness. From a more interdisciplinary perspective, thes…