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Genetic insurance and the value of genetic information
(2024)Losing at the genetic lottery can put you at a higher risk for debilitating illnesses, disability, or early death. This paper investigates the value of learning about one’s risk, e.g. through genetic testing, when the information cannot be used to influence health outcomes. In a 3-periods setting where insurance markets are complete, I show that individuals prefer consumption levels that depend on their risk type, suggesting that genetic ...Working Paper -
Divergent responses of evergreen needle-leaf forests in Europe to the 2020 warm winter
(2024)EGUsphereRelative to drought and heat waves, the effect of winter warming on forest CO2 fluxes during the dormant season has less been investigated, despite its relevance for net CO2 uptake in colder regions with higher carbon content in soils. Our objective was to test the effect of the exceptionally warm winter in 2020 on the winter CO2 budget of cold-adapted evergreen needle-leaf forests across Europe, and identify the contribution of soil and ...Working Paper -
Detection and Mitigation of Glitches in LISA Data: A Machine Learning Approach
(2024)arXivThe proposed Laser Interferometer Space Antenna (LISA) mission is tasked with the detection and characterization of gravitational waves from various sources in the universe. This endeavor is challenged by transient displacement and acceleration noise artifacts, commonly called glitches. Uncalibrated glitches impact the interferometric measurements and decrease the signal quality of LISA's time-delay interferometry (TDI) data used for ...Working Paper -
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sectorgap: An R Package for Consistent Economic Trend Cycle Decomposition
(2024)KOF Working PapersDetermining potential output and the output gap—two inherently unobservable variables—is a major challenge for macroeconomists. This paper presents the R package sectorgap, which features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying economic output fluctuations consistent with subsectors of the economy. The proposed model is able to capture various correlations between output ...Working Paper -
The Language of Hyperelastic Materials
(2024)SAM Research ReportThe automated discovery of constitutive laws forms an emerging area that focuses on automatically obtaining symbolic expressions describing the constitutive behavior of solid materials from experimental data. Existing symbolic/sparse regression methods rely on availability of libraries of material models, which are typically hand-designed by a human expert relying on known models as reference, or deploy generative algorithms with exponential ...Report -
Efficient Computation of Large-Scale Statistical Solutions to Incompressible Fluid Flows
(2024)SAM Research ReportThis work presents the development, performance analysis and subsequent optimization of a GPU-based spectral hyperviscosity solver for turbulent flows described by the three dimensional incompressible Navier-Stokes equations. The method solves for the fluid velocity fields directly in Fourier space, eliminating the need to solve a large-scale linear system of equations in order to find the pressure field. Special focus is put on the ...Report -
Neural Networks for Singular Perturbations
(2024)SAM Research ReportWe prove deep neural network (DNN for short) expressivity rate bounds for solution sets of a model class of singularly perturbed, elliptic two-point boundary value problems, in Sobolev norms, on the bounded interval (−1,1). We assume that the given source term and reaction coefficient are analytic in [−1,1]. We establish expression rate bounds in Sobolev norms in terms of the NN size which are uniform with respect to the singular perturbation ...Report -
Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning
(2024)SAM Research ReportPhysics-informed neural networks (PINNs) and their variants have been very popular in recent years as algorithms for the numerical simulation of both forward and inverse problems for partial differential equations. This article aims to provide a comprehensive review of currently available results on the numerical analysis of PINNs and related models that constitute the backbone of physics-informed machine learning. We provide a unified ...Report -
Exponentially localised interface eigenmodes in finite chains of resonators
(2024)SAM Research ReportThis paper studies wave localisation in chains of finitely many resonators. There is an extensive theory predicting the existence of localised modes induced by defects in infinitely periodic systems. This work extends these principles to finite-sized systems. We consider finite systems of subwavelength resonators arranged in dimers that have a geometric defect in the structure. This is a classical wave analogue of the Su-Schrieffer-Heeger ...Report