Inverse Problems Across Scales
Inverse Problems. Inverse Theory. Inverse Material Design. Full Waveform Inversion. Monte Carlo Methods.
Quantum Disordered Systems.
Predicting the shape of the shadow generated by a 3D object is a straightforward task. However, it can be challenging to solve
the inverse problem of finding the object's shape solely from its projected shadow. Analogous situations appear in many areas
of Physics, either to detect gas reservoirs or to design a new quantum material. In this regard, inverse problems typically
involve extracting physical properties from a noisy and limited information scenario. The typical length scale and the
interactions between physical parameters introduce distinct challenges for each inverse problem. This motivated me to cross
scales in this thesis by analysing four problems and their inverse counterparts. Guided by a cross-fertilization scheme and
following a multiscale approach, I started investigating what mass density a hanging cable must possess to assume a specific
shape. Then, I solve the Bayesian full waveform inversion problem for single and multiple time-dependent seismic surveys. To
address this inverse problem, I employed the Hamiltonian Monte Carlo method with a proposed mass matrix design. After that,
I migrate to the forward problem of determining self-assembled magnetic particles' ordered structures and their collapse
conditions. Finally, I investigate the quantum inverse problem of finding the spatial position of randomly distributed impurities
in a one-dimensional electronic device. These problems provide valuable insights into solving inverse problems and have
applications in various fields of Physics.