Efficient numerical solvers for partial differential equations empower science and
engineering. One commonly employed numerical solver is the preconditioned conjugate …

When learning simulations for modeling physical phenomena in industrial designs,
geometrical variabilities are of prime interest. While classical regression techniques prove …

We introduce a novel, end-to-end learnable, differentiable non-rigid tracker that enables
state-of-the-art non-rigid reconstruction by a learned robust optimization. Given two input …

The use of mixed precision in numerical algorithms is a promising strategy for accelerating
scientific applications. In particular, the adoption of specialized hardware and data formats …

Finding suitable preconditioners to accelerate iterative solution methods, such as the
conjugate gradient method, is an active area of research. In this paper, we develop a …

We present a novel deep learning approach to approximate the solution of large, sparse,
symmetric, positive-definite linear systems of equations. Motivated by the conjugate …

The discovery of fast numerical solvers prompted a clear and rapid shift towards iterative
techniques in many applications, especially in computational mechanics, due to the …

This paper proposes a method to promote power saving in the computational mechanics
simulations by deep learning. The method is expected to contribute to the achievement of …

Recently, correctly handling spatial information from multiple scales has proven to be
essential in Machine Learning (ML) applications on Computational Fluid Dynamics (CFD) …

Many graph-based algorithms in high performance computing (HPC) use approximate
solutions due to having algorithms that are computationally expensive or serial in nature …