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Found 9 papers, 2 papers with code
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Supervised learning has recently garnered significant attention in the field of computational physics due to its ability to effectively extract complex patterns for tasks like solving partial differential equations, or predicting material properties.
Multi-output Gaussian processes for inverse uncertainty quantification in neutron noise analysis
For an ill-posed inverse problem the inverse uncertainty quantification is crucial.
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects
We consider different meta-learners, and we carry out a theoretical analysis of their error upper bounds as functions of important parameters such as the number of treatment levels, showing that the naive extensions do not always provide satisfactory results.
Importance sampling based active learning for parametric seismic fragility curve estimation
The key elements of seismic probabilistic risk assessment studies are the fragility curves which express the probabilities of failure of structures conditional to a seismic intensity measure.
Imaging in random media by two-point coherent interferometry
The goal is to estimate the support of the reflectivity function of a remote scene from measurements of the backscattered wave field.
Dissipation-enhanced collapse singularity of a nonlocal fluid of light in a hot atomic vapor
The theoretical analysis based on the method of characteristics reveals the main counterintuitive result that dissipation (photon losses) is responsible for an unexpected enhancement of the collapse instability.
Quantum Gases Pattern Formation and Solitons Optics
The Climate Extended Risk Model (CERM)
This paper addresses estimates of climate risk embedded within a bank credit portfolio.
Passive communication with ambient noise
We discuss a generalized Helmholtz-Kirchhoff identity that is valid in dispersive media and we characterize the statistical properties of the empirical cross spectral density of the wave field.
Optimal hedging under fast-varying stochastic volatility
It is also shown via numerical simulations that the proposed hedging schemes which derive from option price approximations in the regime of rapid mean reversion, are robust: the `practitioners' delta hedging scheme that is identified as being optimal by our asymptotic analysis when the mean reversion time is small seems to be optimal with arbitrary mean reversion times.
