Spatially aware ESN detects anomalies in chaotic time series.
problem Automated anomaly detection in chaotic time series, especially turbulent ocean simulations.
method Extended Echo State Network with spatially aware input maps and loss function.
result Spatial ESN reduces anomaly detection to thresholding of prediction error.
xVAE models extreme turbulence events in turbulent flows.
problem Capturing extreme events in turbulent flows.
method Max-infinitely divisible process with heavy-tailed distributions embedded into a standard VAE framework.
result xVAE more robust in capturing extreme values compared to POD modes.
Study introduces a probabilistic framework for air-sea fluxes using neural networks.
problem Accurately quantifying air-sea fluxes for understanding interactions and improving weather/climate models.
method Gaussian distributions conditioned on input variables, artificial neural networks, eddy-covariance data, minimizing negative log-likelihood loss.
result Trained neural networks provide alternative mean flux estimates and quantify uncertainty.
Combines ocean surface and interior data to study ocean dynamics.
problem Modeling local ocean currents and global climate patterns.
method Observation-driven framework using Latent-class regression.
result Improved prediction of vertical ocean temperature.
OceanForecastBench offers a comprehensive benchmark for data-driven ocean forecasting models.
problem Lack of open-source, standardized benchmarks for data-driven ocean forecasting models.
method Proposes OceanForecastBench, a benchmark with high-quality data and evaluation pipeline.
result Offers the most comprehensive benchmarking framework for data-driven ocean forecasting.
Lagrangian data assimilation is a complex problem in oceanic and atmospheric modeling. Tracking drifters in large-scale geophysical flows can involve uncertainty in drifter location, complex inertial effects, and other factors which make comparing them to simulated Lagrangian trajectories from numerical models extremel…
Optimizes deep learning models for ocean dynamics using Fourier neural operators.
problem Efficiently training deep learning models for ocean dynamics with optimal hyperparameters.
method Multiobjective hyperparameter optimization with DeepHyper for Fourier neural operators.
result Optimal hyperparameters significantly improved model performance in ocean dynamics forecasting.
Enhances ocean floor mapping with adaptive uncertainty estimates.
problem Inaccurate bathymetric data for precise ocean modeling.
method Block-based conformal prediction with VQ-VAE architecture.
result Significant improvements in reconstruction quality and uncertainty estimation reliability.
We describe tests validating progress made toward acceleration and automation of hydrodynamic codes in the regime of developed turbulence by three Deep Learning (DL) Neural Network (NN) schemes trained on Direct Numerical Simulations of turbulence. Even the bare DL solutions, which do not take into account any physics …
Model accurately gates ocean microbes from high-frequency flow cytometry data.
problem Gating of high-frequency flow cytometry data for ocean microbes is challenging.
method Trend filtered mixture of experts with smooth parameter variation.
result Model accurately matches human-annotated gating and corrects errors.
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.
Paper predicts turbulent flows using physics-informed deep learning.
problem Predicting turbulent flows from fluid simulations.
method Hybrid approach combining RANS and LES with trainable spectral filters and U-net.
result Significant reduction in prediction error for 60 frames ahead.
Gaussian processes improved for ocean current reconstruction and divergence identification.
problem Reconstructing ocean currents from sparse buoy data.
method Proposed a Helmholtz decomposition-based approach to Gaussian processes for better physical modeling.
result Improved inference on ocean currents and divergence identification with minimal computational cost.
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.
Deep learning model predicts wind-wave relationship.
problem Characterize ocean wave climate for engineering applications.
method Two-stage deep learning model: CNN for spatial features, LSTM for temporal dependencies.
result Predicts spatio-temporal relationship between wind and significant wave height.
OCEAN infers online task identities from context variables.
problem Online task inference for compositional tasks with context adaptation.
method Variational inference framework OCEAN models global and local context variables in a joint latent space.
result OCEAN provides more effective task inference with sequential context adaptation.
Study on complex tori foliations and flat geometries.
problem Understanding turbulent foliations on compact complex tori.
method Defined and analyzed smooth turbulent foliations on compact complex tori.
result All transversely holomorphic Cartan geometries are flat.
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.
NN-Turb generates turbulent velocity statistics using neural networks.
problem Creating a 1D field with turbulent velocity statistics.
method Fully-convolutional neural network (NN-Turb) to generate the field.
result NN-Turb generates a 1D field that satisfies Kolmogorov's 2/3 and 4/5 laws, exhibiting intermittency.
Transfer learning improves chaotic dynamics predictions with less data.
problem Efficiently predicting chaotic dynamics with limited data.
method Transfer learning for nonlinear dynamics, optimizing transfer rate and leveraging small-scale turbulence universality.
result Significantly more accurate inference of chaotic dynamics achieved.
Physics-informed ML models improve turbulence understanding in fusion plasmas.
problem Improving turbulence modeling in fusion plasma devices.
method Physics-informed deep learning framework constrained by PDEs.
result Direct quantitative comparisons of turbulent fields between theory and gyrokinetic models.
A neural network models pressure-Hessian from local velocity gradients in turbulent flows.
problem Modeling the pressure-Hessian from local velocity gradients in turbulent flows.
method Tensor basis neural network (TBNN) trained on DNS data.
result Neural network accurately captures key alignment statistics of the pressure-Hessian tensor.
Neural networks predict flow and elastic stresses in viscoelastic turbulence.
problem Predicting flow and elastic stresses in viscoelastic turbulent flows using limited experimental data.
method Convolutional neural networks trained on wall-normal velocity and pressure data.
result Neural networks accurately predict flow and elastic stresses, especially during low-drag events.
This article explores the concepts of ocean wave multivariate multistep forecasting, reconstruction and feature selection. We introduce recurrent neural network frameworks, integrated with Bayesian hyperparameter optimization and Elastic Net methods. We consider both short- and long-term forecasts and reconstruction, f…
Researchers use quantum chaos and RMT to analyze turbulence, revealing unique scaling laws.
problem Understanding the statistical structure and scaling laws of turbulence.
method Applied tools from quantum chaos and Random Matrix Theory to analyze turbulence datasets.
result Turbulence Gram matrices exhibit power-law scalings distinct from classical chaos and random data.
BALLAST optimizes Lagrangian observer placement for ocean vector fields.
problem Optimizing Lagrangian observer placement for time-dependent ocean vector fields.
method Bayesian active learning with look-ahead amendment for sea-drifter trajectories using a physics-informed spatio-temporal Gaussian process surrogate model.
result Noticeable benefits of BALLAST-aided observer placement strategies on synthetic and high-fidelity ocean models.
Study predicts turbulent electric fields in fusion plasmas using deep learning.
problem Predicting turbulent electric fields in fusion plasmas.
method Physics-informed deep learning, drift-reduced Braginskii theory, experimental data.
result Neutrals broaden turbulent field amplitudes and increase shearing rates.
Generative adversarial networks improve subgrid modeling in turbulent reactive flows.
problem Accurately predicting turbulent reactive flows in combustion problems.
method Physics-informed super-resolution GANs trained with unsupervised deep learning.
result Good results in a priori and a posteriori tests with decaying turbulence.
Adversarial reinforcement learning optimizes microswimmers' path-planning in turbulent flows.
problem Optimizing microswimmers' paths in turbulent flows for efficient target reach.
method Adversarial-reinforcement learning scheme applied to 2D and 3D turbulent flows.
result Microswimmers can reach targets faster than a naive approach in turbulent flows.
Study evaluates machine learning methods for uncertainty quantification in complex systems.
problem Accurately quantify epistemic and aleatoric uncertainties in complex dynamical systems.
method Examined Gaussian processes, UQ-augmented neural networks (ENN, BNN, D-NN, G-NN) on two model data sets.
result Concluded on model architecture and hyperparameter tuning for improved UQ accuracy.
Neural network predicts turbulence near-wall regions efficiently.
problem Reducing computational cost in turbulent flow simulations.
method Fully-convolutional neural network trained on DNS data.
result FCN predicts velocity fluctuations at y+=50 with less than 20% error. Three physics-constrained regression exercises for image velocimetry and turbulence modeling.
problem Image velocimetry and turbulence modeling challenges.
method Physics-constrained regression exercises implemented as toy problems.
result Python codes provided for all exercises.
New model combines physics and machine learning for ocean dynamics.
problem Discovering hidden laws governing ocean dynamics.
method Develops Deep Neural Numerical Models (DNNMs) to learn hidden variables of physical laws.
result Illustrates DNNMs applied to Sea Surface Height dynamics, connecting to QG model.
No projective structure found on foliations of elliptic curves.
problem Existence of transversely projective structures on foliations.
method Analyzing foliations on the product of two elliptic curves.
result Generic nonsingular turbulent foliations do not have transversely projective structures.
New turbulence index using TDA detects financial market transitions.
problem Detecting critical transitions in financial markets.
method Persistent homology for identifying topological features.
result Persistent homology-based index captures financial data transitions.
The paper analyzes financial market turbulence using mathematical physics.
problem Understanding price fluctuations caused by information asymmetry.
method Spectrum analysis to decompose pricing patterns.
result Identifies phase correlations in financial stock market turbulence.
Convolutional networks predict turbulence from wall quantities.
problem Predicting turbulence fields from wall-shear-stress components and wall pressure.
method Two CNN models: FCN and FCN-POD, trained on DNS data.
result FCN and FCN-POD models outperform EPOD in predicting turbulence fields.
SIMPGEN improves SWOT SSH data interpretation by removing noise and preserving fine-scale features.
problem Noisy data and limited fine-scale observations in oceanic processes.
method Simulation-Informed Metric and Prior for Generative Ensemble Networks (SIMPGEN) combining real SWOT observations with simulated reference data.
result SIMPGEN effectively removes noise, preserving fine-scale features better than existing neural methods.
Study uses echo-sounder buoys to analyze tuna schools' association with dFADs globally.
problem Understanding temporal trends of tuna schools' association to drifting objects.
method Applied Machine Learning to examine binary and regression outputs of tuna schools' colonization and disaggregation times.
result Median colonization and disaggregation times varied by ocean, with Pacific having longest soak and colonization times.
This paper examines cryptocurrency integration with traditional markets, showing how network structure and turbulence influence cross-asset spillovers.
problem Understanding how cryptocurrencies integrate with traditional financial markets and the impact of market stress on cross-asset spillovers.
method Combining rolling correlation networks, community structure, market-specific and system-wide Turbulence Indices, and VAR-based connectedness analysis.
result Cross-asset integration is episodic, with network structure and turbulence playing a role in transmission during stress periods.
Physics-guided reinforcement learning optimizes swimming in turbulent flows.
problem Optimizing swimming efforts to maintain proximity in turbulent environments.
method Physics-informed actor-physicist reinforcement learning algorithm.
result Physics-informed reinforcement learning outperforms standard methods in turbulent flow control.
HFNO enhances interpretability of turbulent flows through parallel wavenumber bin processing.
problem Opaque inner workings of Fourier Neural Operators (FNOs) hinder physical interpretability.
method Introduces HFNO, a novel FNO-based architecture that processes wavenumber bins in parallel, enhancing interpretability.
result HFNO decomposes turbulent flows across various scales, enabling increased interpretability and multiscale modeling.
Novel spatio-temporal LSTM model forecasts oceanic variables across sensors and scales.
problem Data sparsity and lack of connected spatial and temporal information in environmental datasets.
method SPATIAL LSTM architecture that learns across spatial and temporal scales.
result Framework accurately forecasts oceanic variables with comparable performance to state-of-the-art models.
Study builds an EWS for early detection of stock market turbulence.
problem Early detection of stock market turbulence.
method SWARCH filtering probabilities, two-peak method, LSTM network.
result 96.6% test-set accuracy and 2.4 days of forewarned period.
TURB-Rot provides a large database of turbulent rotating flow snapshots for research.
problem Lack of large-scale, high-resolution datasets for turbulent rotating flows.
method Direct Numerical Simulations of Navier-Stokes equations with rotation.
result Provides a diverse set of 300K complex images and fields for testing.
Study on measurable pseudo-Anosov maps on surfaces.
problem Characterize dynamics of pseudo-Anosov maps on surfaces.
method Analyze measurable pseudo-Anosov homeomorphisms with specific properties.
result Prove transitivity, dense periodic points, sensitivity, and ergodicity.
New family of measurable pseudo-Anosov maps on spheres.
problem Generalizing pseudo-Anosov maps to measurable ones.
method Continuous family of homeomorphisms on sphere, semi-conjugate to core tent map.
result Measurable pseudo-Anosov maps have invariant dense streamlines with uniform measures.
Neural network predicts turbulence from wall shear stress.
problem Predicting wall-bounded turbulence from wall quantities.
method Fully-convolutional neural network trained on DNS data.
result Improved prediction of turbulence fields and statistics.