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Results for “inverse problems”
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Data mining for faster, interpretable solutions to inverse problems: A case study using additive manufacturing
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Extraction of interaction parameters from specular neutron reflectivity in thin films of diblock copolymers: an “inverse problem”
Artificial neural networks are used to extract three Flory-Huggins chi parameters from neutron scattering length density profiles, which paves a way towards automated analysis of neutron reflectivity data.
Uniform-density Bose-Einstein condensates of the Gross-Pitaevskii equation found by solving the inverse problem for the confining potential
Here, in this work, we consider a “reverse-engineering” approach to construct confining potentials that support exact, constant density kovaton solutions to the classical Gross-Pitaevskii equation (GPE) also known as the nonlinear Schr¨odinger equation (NLSE). In the one-dimensional case, the exact solution is the sum of stationary kink and anti-kink solutions, i.e. a kovaton, and in the overlapping region, the density is constant. In higher dimensions, the exact solutions are generalizations of this wave function. In the absence of self-interactions, the confining potential is similar to a smoothed out finite square well with minima also at the edges. When self-interactions are added, a term proportional to ±gψ*ψ gets added to the confining potential and ±gM, where M is the norm, gets added to the total energy. In the realm of stability analysis, we find (linearly) stable solutions in the case with repulsive self-interactions which also are stable to self-similar deformations. For attractive interactions, however, the minima at the edges of the potential get deeper and a barrier in the center forms as we increase the norm. This leads to instabilities at a critical value of M (related to the number of particles in the BEC). Comparing the stability criteria from Derrick’s theorem and Bogoliubov-de Gennes analysis stability results, we find that both predict stability for repulsive self-interactions and instability at a critical mass M for attractive interactions. However, the numerical analysis gives a much lower critical mass. The numerical analysis shows further that the initial instabilities violate the symmetry x → -x assumed by Derrick’s theorem.
CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems
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Preconditioned Pseudo-time Continuation for Parameterized Inverse Problems
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Solving inverse problems for process-structure linkages using asynchronous parallel Bayesian optimization.
Abstract not provided.
Hierarchical off-diagonal Hessian approximation for Bayesian inverse problems with application to the flow of the Greenland ice sheet.
Abstract not provided.
Combining Measure Theory and Bayes? Rule to Solve a Stochastic Inverse Problem.
Abstract not provided.
Risk Averse Optimal Experiment Design using R-Optimality for Vibration Control Inverse Problems.
Abstract not provided.
Solving Stochastic Inverse Problems for Property-Structure Relationships in Computational Materials Science.
Abstract not provided.
Hyper-Differential Sensitivity Analysis of Inverse Problems Governed by PDEs.
Abstract not provided.
Solving inverse problems in process-structure-property linkage with Gaussian process regression.
Abstract not provided.
Enabling and interpreting hyper-differential sensitivity analysis for Bayesian inverse problems.
Abstract not provided.
Beyond forward ICME models: A perspective on materials design as inverse problems.
Abstract not provided.
Constructing Data-consistent Solutions to Stochastic Inverse Problems with Sparse Observable Data.
Abstract not provided.