Study optimizes climate adaptation strategies for NYC.
problem Catastrophic damages from extreme weather in NYC.
method Real options analysis and extreme value theory.
result Optimal adaptation pathways identified for NYC.
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48 results for “sea level rise”
Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fieldsphysics.ao-ph
New neural network enforces mass conservation for better ice flow predictions.
problem Reliably project future sea level rise by improving ice sheet model inputs.
method Proposes divergence-free neural networks (dfNNs) enforcing local mass conservation.
result dfNNs yield more reliable ice flux estimates compared to other models.
Emulation with uncertainty quantification of regional sea-level change caused by the Antarctic Ice Sheetphysics.ao-ph
Neural-network emulators predict sea-level changes due to Antarctic ice melt.
problem High computational cost and time in projecting sea-level changes.
method Built neural-network emulators of sea-level change using GRD effects from future Antarctic Ice Sheet mass change.
result Neural-network emulators are as accurate as baseline machine learning emulators and offer substantial computational efficiency.
Develops probabilistic forecasting for Sea Level Anomalies using Conformal Prediction on functional time series.
problem Forecasting and uncertainty quantification for Sea Level Anomalies.
method Functional data analysis, Conformal Prediction, Functional Autoregressive Processes.
result Proposed method provides accurate probabilistic predictions and uncertainty quantification for Sea Level Anomalies.
Forecast dam inflow using sea surface feature weights.
problem Accurate dam inflow forecasting for flood mitigation.
method Extracted sea surface features, applied L2-norm ensemble weighting, used PCA and t-SNE for dimensionality reduction, and calibrated regression models.
result The proposed method improves predictor stability and accuracy in dam inflow forecasting.
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.
The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. While model-driven representations remain the classic approaches, data-driven representations become more and more appealing to benefit from available large-scale observation and simulation dat…
Generative AI predicts Arctic sea ice dynamics over decades.
problem Reproducing realistic sea ice dynamics from days to decades is computationally challenging.
method Introduced GenSIM, a generative AI model trained on 20 years of sea-ice-ocean simulation data.
result Generative AI predicts realistic sea ice evolution for 30 years, capturing long-term trends and physical consistency.
We study the Gaussian Process regression model in the context of training data with noise in both input and output. The presence of two sources of noise makes the task of learning accurate predictive models extremely challenging. However, in some instances additional constraints may be available that can reduce the unc…
Understanding local currents in the North Atlantic region of the ocean is a key part of modelling heat transfer and global climate patterns. Satellites provide a surface signature of the temperature of the ocean with a high horizontal resolution while in situ autonomous probes supply high vertical resolution, but horiz…
New method corrects seasonal Arctic sea ice predictions with probabilistic models.
problem Systematic biases and errors in climate model forecasts of Arctic sea ice.
method Conditional Variational Autoencoder model to map observation distribution given biased model predictions.
result Probabilistic adjusted forecasts are better calibrated and have smaller errors.
Paper introduces MTCM to measure multivariate tail dependence.
problem Classical TDC fails to capture non-exchangeable features of multivariate tail dependence.
method Extends bivariate tail copula measure to multivariate case.
result MTCM reveals off-diagonal stress directions and differences in extremal dependence.
New model uses heteroscedastic Gaussian process for alkenone SST proxy.
problem Restoring historical sea surface temperatures using proxies.
method Heteroscedastic Gaussian process regression method.
result Nonparametric approach handles variable noise patterns and outliers.
Decision tool helps manage biofouling risks for ships in the Baltic Sea.
problem Biofouling of ships causes environmental and economic issues.
method Bayesian networks to identify biofouling management strategies.
result Optimal biofouling management includes biocidal-free coating and in-water cleaning.
A Comparison of Machine Learning Algorithms for Predicting Sea Surface Temperature in the Great Barrier Reef Regionphysics.ao-ph
Study compares machine learning algorithms for predicting SST in the Great Barrier Reef.
problem Predicting sea surface temperature in the Great Barrier Reef region.
method Ridge regression, LASSO, Random Forest, and Extreme Gradient Boosting (XGBoost) algorithms were evaluated.
result XGBoost significantly outperforms other algorithms in terms of predictive accuracy and Kullback-Leibler Divergence.
Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…
A theorem connects integral of second-order derivatives to function rise.
problem Understanding the integral of second-order derivatives over regions.
method Proves integral proportional to function rise over specified regions.
result Integral of second-order derivatives equals rise in function value.
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimizationcs.LG
Improved regret bounds for online convex optimization under stochastic and adversarial settings.
problem Interpolating between stochastic and adversarial online convex optimization.
method Optimistic online mirror descent (OMD) for the Stochastically Extended Adversarial (SEA) model.
result Established new regret bounds for various function classes.
Method reconstructs glacier front trajectories from record moraine data.
problem Understanding past glacier dynamics from limited record data.
method Stochastic generator based on Brownian motion and NBI hyper parameter tuning.
result Reconstructed glacier front trajectories from moraine records.
CVAE detects weak complex signals in maritime radar, improving detection over classical methods.
problem Detecting weak complex-valued signals in non-Gaussian, range-varying interference.
method Complex-valued Variational AutoEncoder (CVAE) trained on clutter-plus-noise, whitening, ANMF fusion.
result CVAE yields higher detection probability Pd at matched false-alarm rate Pfa, especially with whitening.
Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing datastat.ML
Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal conv…
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.
Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering. In this paper, we present Graph-Regularized Image Semantic Embedding (Graph-RISE), a large-scale neural graph learning framework that allows us to tr…
We investigate three-dimensional surfaces where the normal vector forms a constant angle with the radius vector. These surfaces naturally extend equiangular (logarithmic) spirals in the plane.
We introduce a new strategy designed to help physicists discover hidden laws governing dynamical systems. We propose to use machine learning automatic differentiation libraries to develop hybrid numerical models that combine components based on prior physical knowledge with components based on neural networks. In these…
Pattern Ensembling for Spatial Trajectory Reconstructionphysics.data-an
Pattern ensembling fills in missing or inaccurate trajectory data.
problem Incompleteness, missing information, and inaccuracies in geolocation data.
method Probabilistically ensemble similar trajectory patterns from the vicinity.
result Reconstructs missing or unreliable trajectory segments effectively.
Designing a covariance function that represents the underlying correlation is a crucial step in modeling complex natural systems, such as climate models. Geospatial datasets at a global scale usually suffer from non-stationarity and non-uniformly smooth spatial boundaries. A Gaussian process regression using a non-stat…
Modeling Climate Change Impact on Wind Power Resources Using Adaptive Neuro-Fuzzy Inference Systemphysics.ao-ph
Study uses ANFIS to assess wind power under climate change.
problem Tackles climate change impact on wind power potential.
method Employed ANFIS to match climate model data with reference data.
result Real wind power potential lower than projected.
New algorithms compute Koopman operators on RKHSs efficiently and accurately.
problem Data-driven spectral analysis of Koopman operators on RKHSs.
method General, provably convergent algorithms for RKHSs.
result Optimal algorithms with error control and spectral measures.
We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive paradigm could begin to challenge more traditional approaches elaborated over the years in fields like maths or phy…
A Novel Efficient Approach with Data-Adaptive Capability for OMP-based Sparse Subspace Clusteringcs.LG
Orthogonal Matching Pursuit (OMP) plays an important role in data science and its applications such as sparse subspace clustering and image processing. However, the existing OMP-based approaches lack of data adaptiveness so that the data cannot be represented well enough and may lose the accuracy. This paper proposes a…
Neural point estimators improve parameter estimation from replicated data.
problem Making inference from replicated data in weakly-identified and highly-parameterised models.
method Permutation-invariant neural networks for likelihood-free parameter estimation.
result Neural point estimators can quickly and optimally estimate parameters.
We show that the climate phenomena of El Nino and La Nina arise naturally as states of macro-variables when our recent causal feature learning framework (Chalupka 2015, Chalupka 2016) is applied to micro-level measures of zonal wind (ZW) and sea surface temperatures (SST) taken over the equatorial band of the Pacific O…
New method for PKM inverse dynamics second derivatives efficiently.
problem Efficient computation of PKM inverse dynamics second derivatives.
method Recursive Lie-group formulation for serial robots adapted to PKM topology.
result Efficient computation of second time derivatives for PKM.
In this study, the wind data series from five locations in Aegean Sea islands, the most active `hotspots' in terms of refugee influx during the Oct/2015 - Jan/2016 period, are investigated. The analysis of the three-per-site data series includes standard statistical analysis and parametric distributions, auto-correlati…
DYffusion improves diffusion models for spatiotemporal forecasting.
problem Challenges in generating stable and accurate forecasts for dynamic data.
method Leverages temporal dynamics in data, directly coupling it with diffusion steps.
result Improves computational efficiency and performs competitively on complex dynamics.
Proposes a method to enhance exploration in RL using temporal difference uncertainties.
problem Challenges in estimating uncertainty in non-tabular reinforcement learning settings.
method Estimates uncertainty over value function using temporal difference errors and incorporates it as an intrinsic reward.
result Demonstrates improved exploration in various tasks, including Deep Sea and Atari 2600 environments.
As machine learning models are increasingly used for high-stakes decision making, scholars have sought to intervene to ensure that such models do not encode undesirable social and political values. However, little attention thus far has been given to how values influence the machine learning discipline as a whole. How …
CCVA adjusts for climate change impacts on financial valuation.
problem Climate change impacts on financial valuation are currently ignored.
method Flexible parameterization to capture climate impacts on hazard rates.
result Significant impacts on interest rate swaps even with slow climate change.
Pencils of geodesics in symmetric spaces, Karpelevich boundary, and associahedron-like polyhedramath.DG
There are several ways of a construction of a boundary of a symmetric space using pencils of geodesics: the Karpelevich boundary, the visibility boundary, the associahedral boundary, and the sea urchin. We give explicit descriptions of these boundaries. We obtain some moduli space like polyhedra as sections of these co…
Motivated by the idea of turbomachinery active subspace performance maps, this paper studies dimension reduction in turbomachinery 3D CFD simulations. First, we show that these subspaces exist across different blades---under the same parametrization---largely independent of their Mach number or Reynolds number. This is…
TRAKNN: Efficient Trajectory Aware Spatiotemporal kNN for Rare Meteorological Trajectory Detectionstat.ML
TRAKNN detects rare atmospheric trajectories efficiently.
problem Detecting rare atmospheric anomalies over long periods.
method Unsupervised, recurrence-based kNN algorithm.
result Rare trajectories correspond to physical anomalies.
Adaptive Batching for Gaussian Process Surrogates with Application in Noisy Level Set Estimationstat.ML
Adaptive batching improves Gaussian process surrogates for noisy level set estimation.
problem Learning the level set of noisy simulator responses.
method Developed four novel adaptive batching schemes for Gaussian process metamodels.
result Adaptive batching brings significant computational speed-ups with minimal loss of modeling fidelity.
Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.
problem Binary prediction sets are limited; fuzzy prediction sets offer richer guarantees.
method Generalize prediction sets to fuzzy sets, showing they are e-values with merging properties.
result Optimal e-values lead to optimal fuzzy prediction sets, including optimal conformal prediction.
We introduce an agent-based model, in which agents set their prices to maximize profit. At steady state the market self-organizes into three groups: excess producers, consumers and balanced agents, with prices determined by their own resource level and a couple of macroscopic parameters that emerge naturally from the a…
Flexible XVAE model for efficient spatial extremes simulation.
problem Complex tail dependence structures in spatial extremes processes.
method Variational autoencoder (XVAE) for modeling flexible and non-stationary dependence.
result XVAE provides fast inference and outperforms traditional models in high dimensions.
This review assesses statistical and machine learning methods for coral bleaching.
problem Coral bleaching due to rising sea temperatures and environmental factors.
method Statistical and machine learning models for predicting and analyzing coral bleaching.
result Statistical and machine learning methods are crucial for effective reef management.
Over the past decade, the stellar growth of Indian economy has been challenged by persistently high levels of inflation, particularly in food prices. The primary reason behind this stubborn food inflation is mismatch in supply-demand, as domestic agricultural production has failed to keep up with rising demand owing to…