Topology Optimization and Physics-Informed Neural Networks for Metamaterial Optics Design

The development of optical metamaterials in recent years has enabled the design of novel optical devices with exciting properties and applications ranging across many fields, including in scientific instrumentation for space missions. This in<br/>turn has led to demand for computational methods which can produce efficient device designs....

Automatic Differentiation based Multi-Mode Ptychography: A flexible and highly efficient lensless imaging algorithm

The scientific community recognizes the critical role played by ptychography in nanoscale imaging. Compared with the conventional imaging, which has high requirements on the manufacturing of optical elements, ptychography, as a computational imaging technique, uses a set of measured intensities of the diffraction patterns to reconstruct the...

Automated Medical History Taking in Primary Care: A Reinforcement Learning Approach

Online searching for healthcare information has gradually become a widely used internet case. Suppose a patient suffers the symptom but is unsure of the action he needs to take, a self-diagnosis tool can help the patient identify the possible conditions and whether this patient needs to seek immediate medical help. However, the accuracy and...

The application of differentiable programming frameworks to computational fluid dynamics

In recent years many automatic differentiable programming frameworks have been developed in which numerical programs can be differentiated through automatic differentiation (AD). Examples of these frameworks are Theano, TensorFlow and Pytorch. These frameworks are widely used in Machine Learning. AD also finds applications in the field of...

Multiscale gradient computation for flow in heterogeneous porous media

An efficient multiscale (MS) gradient computation method for subsurface flow management and optimization is introduced. The general, algebraic framework allows for the calculation of gradients using both the Direct and Adjoint derivative methods. The framework also allows for the utilization of any MS formulation that can be algebraically...

Adjoint formulation and constraint handling for gradient-based optimization of compositional reservoir flow

An adjoint formulation for the gradient-based optimization of oil-gas compositional reservoir simulation problems is presented. The method is implemented within an automatic differentiation-based compositional flow simulator (Stanford's AD-GPRS). The development of adjoint procedures for general compositional problems is much more challenging...

Manifold mapping optimization with of without true gradients

This paper deals with the Space Mapping optimization algorithms in general and with the Manifold Mapping technique in particular. The idea of such algorithms is to optimize a model with a minimum number of each objective function evaluations using a less accurate but faster model. In this optimization procedure, fine and coarse models interact...

The use of automatic differentiation for adjoint CFD codes

Optimisation of aerospace designs uses the linear gradients of the minimised objective functionals with respect to the specified design variables. These gradients can be computed using a linearised version (or the adjoint equivalent) of the CFD code that outputs the original objective functionals. The task of generating linear or adjoint code...

Enabling User-Driven Checkpointing Strategies in Reverse-Mode Automatic Differentiation

This paper presents a new functionality of the Automatic Differentiation (AD) Tool Tapenade. Tapenade generates adjoint codes which are widely used for optimization in CFD. Unfortunately, for large applications the adjoint code demands a great deal of memory, because it needs to store a large set of intermediates values. To cope with that...

Computation of topological sensitivities in fluid dynamics: Cost function versatility

Topology optimization of fluid dynamical systems is still in its infancy, with its first academic realizations dating back to as late as three years ago. In this paper, we present two approaches to fluid dynamic topology optimization that are based on potential flows and adjoint states, respectively. Special emphasis is paid to the computation...