A proposal to redefine design in a way that not only challenges the field's dominant
paradigms but also changes the practice of design itself. In Critical Fabulations, Daniela …

We present an approach for using machine learning to automatically discover the governing
equations and unknown properties (in this case, masses) of real physical systems from …

This paper is a survey of inductive rule learning algorithms that use a separate-and-conquer
strategy. This strategy can be traced back to the AQ learning system and still enjoys …

The physical characteristics and atmospheric chemical composition of newly discovered
exoplanets are often inferred from their transit spectra, which are obtained from complex …

SimStudent is a machine-learning agent initially developed to help novice authors to create
cognitive tutors without heavy programming. Integrated into an existing suite of software …

Declarative bias plays an important role when learning in potentially huge hypothesis
spaces. While scienti c discovery systems, which perform equation discovery as a subtask …

Existing genetic algorithms (GA) based systems for solving time-cost trade-off problems
suffer from two limitations. First, these systems require the user to manually craft the time …

In this paper, we introduce PDE-LEARN, a novel deep learning algorithm that can identify
governing partial differential equations (PDEs) directly from noisy, limited measurements of …

Abstract Machine discovery systems help humans to find natural laws from collections of
experimentally collected data. Most of the laws found by existing machine discovery systems …

We demonstrate the use of symbolic regression in deriving analytical formulas, which are
needed at various stages of a typical experimental analysis in collider phenomenology. As a …