Improved spectral embedding using wave simulation for data analysis.
problem Data dimensionality reduction and metric refinement.
method Wave simulation of eigenfunctions of the discrete Laplacian to refine metric for dimensionality reduction.
result Improved results for data embedding and dimensionality reduction.
Neural networks improve wave-equation simulation accuracy.
problem Inaccurate discretization of Laplacian in wave-equation simulation leads to numerical dispersion.
method Intersperse CNNs between low-fidelity timesteps to correct wavefield and limit numerical dispersion.
result Neural network augmentation reduces numerical dispersion artifacts in wave-equation simulation.
Simulation-based inference speeds up gravitational wave data analysis.
problem High-dimensional parameter spaces and complex noise in gravitational wave data.
method Simulation-based inference methods using machine learning techniques.
result Simulation-based inference methods improve speed over traditional methods.
Neural network predicts wave propagation from limited data.
problem Predicting wave behavior from minimal observations.
method Deep neural network with encoder, propagator (3 LSTMs), and decoder.
result Reasonable predictions up to 80 time steps, generalizes to new conditions.
Deep learning improves damage localization in ultrasonic waves under uncertainty.
problem Uncertainty in wave propagation due to environmental factors and noise.
method Deep learning model trained on simulated wave data with uncertainty.
result Deep learning model learns robust representations of wave data with uncertainty.
The paper proposes a system to accurately locate devices using millimeter wave radiation.
problem Accurate positioning of devices in urban environments with millimeter wave communications.
method Beamformed fingerprint learning using deep learning and pre-established beamforming patterns.
result Average estimation errors below 10 meters achievable in outdoor scenarios.
Deep learning speeds sound speed inversion in ultrasound.
problem Limited high-end ultrasound hardware for shear wave imaging.
method Fully convolutional deep neural network using simulated data.
result Inversion of longitudinal sound speed at high frame rates.
New method for efficient inference over complex parameter spaces.
problem Challenges in Bayesian inference for high-dimensional, intractable likelihoods.
method Arbitrary Marginal Neural Ratio Estimation (AMNRE) for simulation-based inference.
result Efficient inference over arbitrary subsets of parameters without numerical integration.
Constraint-aware neural networks improve accuracy in fluid flow simulations.
problem Ensuring physical constraints in neural network simulations for fluid dynamics.
method Two strategies to create constraint-aware neural networks for Riemann problems.
result Decrease in constraint deviation correlates with low discretization errors.
Machine learning speeds up GPR simulations.
problem Computational demands of simulating practical GPR problems.
method Automatic ML-based forward solver framework using gprMax.
result Near-real-time GPR simulations achieved.
Deep learning speeds up material property quantification using stress waves.
problem Quantifying material properties from stress waves in complex media.
method Surrogate deep learning FWI scheme trained on random sampled properties and local minima.
result Demonstrates feasibility of deep learning for high-accuracy material property estimation.
This study compares RNN and CNN for predicting wave propagation using the Saint-Venant equations.
problem Predicting wave propagation over long time periods using deep learning.
method Investigated recurrent and convolutional neural networks for their performance in predicting surface waves governed by the Saint-Venant equations.
result Convolutional networks perform at least as well as recurrent networks in predicting wave propagation.
Regression algorithm uses quantum annealer for sparse inference in lattice QCD simulations.
problem Improving prediction accuracy in lattice QCD simulations.
method Proposes a regression algorithm that combines sparse inference with a D-Wave quantum annealer.
result Quantum annealer improves prediction performance in lattice QCD simulations.
Study uses DMD to analyze oceanic features in Strait of Gibraltar.
problem Understanding complex oceanic features in Strait of Gibraltar.
method Dynamic Mode Decomposition (DMD) applied to 3D MIT general circulation model simulations.
result Unveiled new elements and dynamics of the Strait of Gibraltar, including a secondary gyre and wave propagation.
Study analyzes 3,171 stocks to pick efficient portfolios using quantum and classical solvers.
problem Creating efficient stock portfolios from a large dataset.
method Used classical and quantum solvers to optimize portfolios of 3,171 US stocks.
result Demonstrated the effectiveness of quantum and classical solvers in portfolio optimization.
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.
A machine-learning method speeds up RIS design by predicting reflection coefficients.
problem Extensive full-wave EM simulations are time-consuming for RIS design.
method Combining MLP and dual-port network to develop a fast model.
result The proposed method significantly reduces the time for RIS design.
Beginning with several basic hypotheses of quantum mechanics, we give a new quantum model in econophysics. In this model, we define wave functions and operators of the stock market to establish the Schrödinger equation for the stock price. Based on this theoretical framework, an example of a driven infinite quantum wel…
A discrete method approximates hyperbolic curvature flow in the plane.
problem Modeling wave phenomena in solid-liquid interfaces.
method Semidiscrete finite difference method for hyperbolic curvature flow.
result Error bounds for natural discrete norms are proven.
Bayesian geoacoustic inversion improved using MDN.
problem Efficiently solving Bayesian geoacoustic inversion problems.
method Deriving geoacoustic statistics from multidimensional posterior density using MDN, training the network on the whole parameter space.
result The network provides reliable predictions and good generalization performance, solving problems in seconds.
Method identifies financial rogue waves close to their onset.
problem Identifying extreme financial events close to their onset.
method Analogy between rogue waves in optics and financial volatility, using Schrödinger equation with potential shaped by Kerr nonlinearity.
result Numerical gradient spikes at the onset of extreme financial events.
SWD approximates GP for faster regression.
problem Costly GP matrix inversion limits large data analysis.
method SWD replaces matrix inversion with diagonalization for grid and short-range correlations.
result SWD-GP outperforms existing methods in predictive accuracy.
Extended wMEM approach for MEG inverse problem using wavelet and spatial filters.
problem Infer brain activity from full space-time data in MEG.
method Wavelet decomposition, spatial filters, Kronecker product modeling, numerical optimization.
result Smooth numerical optimization problem solved with reasonable dimensionality.
Deep learning simplifies nanophotonic device physics.
problem Understanding light-matter interactions in nanophotonic devices.
method Deep learning algorithm with dimensionality reduction.
result Extracts useful information about nanostructure features.
Physics-constrained GP predicts material states under shockwave conditions.
problem Predicting material states under extreme shockwave conditions.
method Physics-constrained Gaussian Process regression with Rankine-Hugoniot constraints.
result Reproduces Hugoniot curves with satisfactory accuracy and uncertainty quantification.
ML surrogates speed up Bayesian inverse problem solving.
problem Infer source location from noisy acoustic wave equation data.
method Use neural network as surrogate for PDE, apply MCMC to posterior.
result Accurately infers source location from noisy data.
GNPE improves inference for astrophysical systems.
problem Efficiently incorporating geometric properties like equivariances in neural density estimation.
method GNPE integrates equivariances into neural posterior estimation, standardizing data pose while estimating parameters.
result GNPE achieves state-of-the-art accuracy in astrophysical binary black hole inference, reducing inference times by 3 orders of magnitude.
Study wave front singularities and their geometric properties.
problem Characterize singularities of focal surfaces of wave fronts.
method Characterization through differential geometric properties.
result Relationships between focal surfaces and initial wave fronts' geometric invariants.
Study proves interaction of three impulsive gravitational waves, showing local solution and Lipschitz continuity.
problem Interaction of three impulsive gravitational waves in Einstein vacuum equations.
method Geometric estimates and wave estimates to prove local solution and continuity.
result Local solution to Einstein vacuum equations with three impulsive gravitational waves, Lipschitz continuity.
New Galilean spacetimes found as pp-wave reductions.
problem Understanding isotropic homogeneous Galilean spacetimes.
method Null reductions of pp-wave spacetimes.
result Found novel torsional Galilean spacetimes.
Lossy compression of statistical data using quantum annealing.
problem Efficiently compressing statistical floating-point data.
method Representation learning with binary variables, classical optimization of basis vectors, quantum annealing for coefficients, bias correction.
result Quantum annealing shows promising results with 3.5x better compression than neural-network autoencoders.
Analyzes Gerstner's trochoidal waves and their geometric properties.
problem Understanding the geometry and kinematics of trochoidal waves.
method Derives velocity and arc length conditions for cycloidal, curtate, and prolate trochoids using Galilean transformations.
result Conditions for arc lengths of prolate and curtate trochoids to coincide over a wave cycle.
Wave fronts on certain surfaces become dense.
problem Density of wave fronts on surfaces.
method Proof of density for specific surfaces.
result Wave fronts become dense on flat torus, square billiard, Klein bottle, and cube surface.
New method bypasses global fit for LISA's Galactic binaries, extracting population parameters directly.
problem Disentangling LISA's Galactic binary sources from backgrounds in a computationally intensive process.
method Simulation-based approach using normalizing flow to infer population parameters.
result Direct inference of population parameters from LISA's frequency strain series.
Investigates curvature properties of generalized pp-wave metric.
problem Examines curvature characteristics of generalized pp-wave metric.
method Analyzes Ricci, quasi-Einstein, and pseudosymmetric properties.
result Shows various curvature properties and sufficient conditions.
Analyzes properties of stiffness tensors for elastic wave imaging.
problem Characterizing stiffness tensor fields for elastic wave imaging.
method Finsler-geometric methods applied to anisotropic stiffness tensor fields.
result Conditions for Finsler-geometric methods to be applicable.
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.
Impulsive waves contradict a 1962 conjecture about pp-waves.
problem The failure of the Ehlers--Kundt conjecture in the impulsive case.
method Summarized completeness results for impulsive wave spacetimes.
result Impulsive pp-waves are complete, contradicting the conjecture.
Extends Penrose limit to Finsler spacetimes.
problem Extending Penrose limit to Finsler spacetimes.
method Introducing lightlike coordinates and adapting Lorentzian pp-wave definition.
result New examples of Finsler pp-waves presented.
New findings on plane waves in 3D spacetimes, showing non-unimodular elliptic plane waves are unique.
problem Classifying Lorentz homogeneous spaces of dimension 3, focusing on plane waves.
method Revisiting and relaxing usual completeness assumptions, characterizing homogeneous plane waves.
result Non-unimodular elliptic plane waves are unique and non-extendable, geodesically complete only if symmetric.
Classifies solutions to vacuum weighted Einstein equations on pr-waves.
problem Classifying solutions to vacuum weighted Einstein field equations on pr-waves.
method Classifying solutions using smooth metric measure spacetimes of dimension 4.
result Provides examples of solutions with special geometric properties.
High frequency limit for most of wave phenomena is known as quasiclassical limit or ray optics limit. Propagation of waves in this limit is described in terms of wave fronts and rays. Wave front is a surface of constant phase whose points are moving along rays. As it appears, their motion can be described by Hamilton e…
Study compact plane waves, showing they are essentially standard.
problem Understanding the topology and dynamics of compact plane waves.
method Analyzing quotients of homogeneous plane waves by discrete subgroups.
result Compact quotients of homogeneous plane waves are essentially standard.
New method finds precise late-time behavior of wave equations.
problem Analyzing late-time behavior of wave equations with inverse-square potentials.
method Physical-space-based method for deriving late-time asymptotics.
result Sharp, uniform decay estimates in time for asymptotic late-time tails.
Paper explores non-uniqueness and uniqueness class for wave equations on graphs.
problem Non-uniqueness of solutions to wave equations on infinite graphs.
method Analyticity of solutions in the uniqueness class, extension to a wide class of linear evolution equations.
result Sharp uniqueness class for solutions of wave equations on graphs.
New metrics link Kähler and pp-wave spacetimes.
problem Connecting Kähler and pp-wave spacetimes.
method Constructing families of complete almost Kähler metrics by deforming pp-waves.
result Established a one-to-one correspondence between almost Kähler metrics and pp-wave spacetimes.
The financial rogue waves are reported analytically in the nonlinear option pricing model due to Ivancevic, which is nonlinear wave alternative of the Black-Scholes model. These solutions may be used to describe the possible physical mechanisms for rogue wave phenomenon in financial markets and related fields.
Machine learning predicts dam-break flood wave behavior accurately.
problem Predicting long-term wave behavior in dam-break floods.
method Solved Saint-Venant equations using Lax-Wendroff scheme, trained RC-ESN with flow depth data.
result RC-ESN model predicts 286 time-steps ahead with RMSE < 0.01, outperforming LSTM.