We propose a distributional formulation of the spanning problem of a multi-asset payoff by vanilla basket options. This problem is shown to have a unique solution if and only if the payoff function is even and absolutely homogeneous, and we establish a Fourier-based formula to calculate the solution. Financial payoffs are typically piecewise linear, resulting in a solution that may be derived explicitly, yet may also be hard to exploit numerically. One-hidden-layer feedforward neural networks instead provide a natural and efficient numerical alternative for discrete spanning. We test this approach for a selection of archetypal payoffs and obtain better hedging results with vanilla basket options compared to industry-favored approaches based on single-asset vanilla hedges.
We consider the scattering of a time-harmonic plane wave incident on a two-scale heterogeneous medium, which consists of scatterers that are much smaller than the wavelength and extended scatterers that are comparable to the wavelength. A generalized Foldy-Lax formulation is proposed to capture multiple scattering among point scatterers and extended scatterers. Our formulation is given as a coupled system, which combines the original Foldy-Lax formulation for the point scatterers and the regular boundary integral equation for the extended obstacle scatterers. An efficient physically motivated Gauss-Seidel iterative method is proposed to solve the coupled system, where only a linear system of algebraic equations for point scatterers or a boundary integral equation for a single extended obstacle scatterer is required to solve at each step of iteration. In contrast to the standard inverse obstacle scattering problem, the proposed inverse scattering problem is not only to determine the shape of the extended obstacle scatterer but also to locate the point scatterers. Based on the generalized Foldy-Lax formulation and the singular value decomposition of the response matrix constructed from the far-field pattern, an imaging function is developed to visualize the location of the point scatterers and the shape of the extended obstacle scatterer.
Photoacoustic tomography (PAT) is an emerging soft-tissue imaging modality that has great potential for a wide range of biomedical imaging applications. It can be viewed as a hybrid imaging modality in the sense that it utilizes an optical contrast mechanism combined with ultrasonic detection principles, thereby combining the advantages of optical and ultrasonic imaging while circumventing their primary limitations. The goal of PAT is to reconstruct the distribution of an object's absorbed optical energy density from measurements of pressure wavefields that are induced via the thermoacoustic effect. In this talk, we review our recent advancements in practical image reconstruction approaches for PAT in heterogeneous acoustic media. Such advancements include physics-based models of the measurement process and associated inversion methods for reconstructing images from limited data sets. Applications of PAT to transcranial brain imaging are presented.
We prove that if for the isotropic Lamé system the coefficiem $\mu$ is a positive constant then both coefficents can be reconstructed from the partial Cauchy data.
Many techniques developed for free-discontinuity problems, arising for example in imaging or in fracture mechanics, may be successfully applied to reconstruction methods for inverse problems whose unknowns may be characterized by discontinuous functions.
We show the validity of this approach both from the theoretical point of view, by a convergence analysis, and from the numerical point of view.
In this talk, we shall consider the near-invisibility cloaking in acoustic scattering by non-singular transformation media. A general lossy layer is included into our construction. We are especially interested in the cloaking of active/radiating objects. Our results on the one hand show how to cloak active contents more efficiently, and on the other hand indicate how to choose the lossy layer optimally.
Waves reflecting/refracting/transmitting from singularities of a metric (e.g. sound speed) satisfy the law of reflection. One expects that if the singularities are sufficiently weak, in terms of differentiability (conormal order) then the reflected singularity is weaker than the transmitted one, in the sense that it is more regular. In this joint work with Maarten de Hoop and Gunther Uhlmann we prove such a result with slightly more regular than C^1 metrics.
We consider boundary measurements for the wave equation on a bounded domain $M \subset \R^2$ or on a compact Riemannian surface, and introduce a method to locate a discontinuity in the wave speed. Assuming that the wave speed consist of an inclusion in a known smooth background, the method can determine the distance from any boundary point to the inclusion. In the case of a known constant background wave speed, the method reconstructs a set contained in the convex hull of the inclusion and containing the inclusion. Even if the background wave speed is unknown, the method can reconstruct the distance from each boundary point to the inclusion assuming that the Riemannian metric tensor determined by the wave speed gives simple geometry in $M$. Computationally the method consists of solving a sequence of linear equations. We present some numerical results.
We present a numerical study of the quantitative photoacoustic
tomography problem with the transport model, aiming at reconstructing simultaneously the absorption, scattering and Gr\"uneisen coefficients with interior data. We study the effect of the amount of data on the quality of the reconstructions, and investigate related uniqueness and non-uniqueness issues. We propose simple reconstruction procedures in some specific cases. Numerical simulations with synthetic data will be presented.