Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

Full-waveform inversion (FWI) can be made immune to cycle skip** by matching the
recorded data with traveltime errors smaller than one-half period from inaccurate subsurface …

We formulate a general framework for hp-variational physics-informed neural networks (hp-
VPINNs) based on the nonlinear approximation of shallow and deep neural networks and …

We introduce Devito, a new domain-specific language for implementing high-performance
finite-difference partial differential equation solvers. The motivating application is exploration …

We consider variants of trust-region and adaptive cubic regularization methods for non-
convex optimization, in which the Hessian matrix is approximated. Under certain condition …

Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization
problem to reduce cycle skip** in full-waveform inversion (FWI). WRI is often implemented …

Chaotic maps have the characteristics of ergodicity and non-repeatability. Owing to these
properties, they provide fast convergence by effectively scanning the search space in a …

Separable nonlinear least-squares (SNLLS) problems arise frequently in many research
fields, such as system identification and machine learning. The variable projection (VP) …

Full-waveform inversion (FWI) is an iterative nonlinear waveform matching procedure
subject to wave-equation constraint. FWI is highly nonlinear when the wave-equation …

We propose an extended full-waveform inversion formulation that includes general convex
constraints on the model. Though the full problem is highly nonconvex, the overarching …