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Recent advancements in machine learning techniques for protein structure prediction motivate better results in its inverse problem–protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in tandem, allowing to improve protein backbone design when the structure is better estimated. We use the ProteinNet data set and show that the state of the art results in protein design can be met and even improved, given recent architectures for protein folding.

Recent advancements in machine learning techniques for protein folding motivate better results in its inverse problem -protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in tandem, allowing to improve protein design when the structure is better estimated. We use the ProteinNet data set and show that the state of the art results in protein design can be improved, given recent architectures for protein folding.

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In many cases, inversion in 2D gives a better description of the subsurface compared with 1D inversion, but, computationally, 2D inversion is expensive, and it can be hard to use for large-scale surveys. We have developed an efficient hybrid 2D airborne frequency-domain electromagnetic inversion algorithm. Our hybrid scheme combines 1D and 2D inversions in a three-stage process, in which each step is progressively more accurate and computationally more expensive than the previous one. This results in an approximately 2x − 6x speedup compared with full 2D inversions, and with only minor changes to the inversion results. Our inversion structure is based on a regular grid, in which each sounding is discretized individually. The 1D modeling code uses layered models with derivatives derived through the finite-difference method, whereas our 2D modeling code uses an adaptive finiteelement mesh, and it uses the adjoint-state method to calculate the derivatives. By incorporating the inversion grid structure into the 2D finite-element mesh, interpolation between the different meshes becomes trivial. Large surveys are handled by using local meshing to split large surveys into small sections, which retains the 2D information. The algorithm is heavily optimized and parallelized over the frequencies and sections, with good scalability even on nonuniform memory architecture systems, on which it is generally hard to achieve a satisfactory scaling. The algorithm has been tested successfully with various synthetic studies as well as field examples, of which results from two synthetic studies and a field example are shown.