Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and
powerful computational resources, impacting many fields, including protein structural …

Due to the increasing size of integrated circuits (ICs), their design and optimization phases
(ie, computer-aided design, CAD) grow increasingly complex. At design time, a large design …

Recent improvements in conditional generative modeling have made it possible to generate
high-quality images from language descriptions alone. We investigate whether these …

Efficiently navigating a superpressure balloon in the stratosphere requires the integration of
a multitude of cues, such as wind speed and solar elevation, and the process is complicated …

In the last few years, graph neural networks (GNNs) have become the standard toolkit for
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …

Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …

Including prior knowledge is important for effective machine learning models in physics and
is usually achieved by explicitly adding loss terms or constraints on model architectures …

Recently, deep reinforcement learning (DRL) frameworks have shown potential for solving
NP-hard routing problems such as the traveling salesman problem (TSP) without problem …

A central aspect for operating future quantum computers is quantum circuit optimization, ie,
the search for efficient realizations of quantum algorithms given the device capabilities. In …

In this paper, we aim to solve data-driven model-based optimization (MBO) problems, where
the goal is to find a design input that maximizes an unknown objective function provided …