Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
This research investigates the application of reinforcement learning to automatically generate and optimize geometric patterns, focusing on identifying novel patterns exhibiting complex, self-organizing behavior. Traditional geometric pattern generation often relies on predefined rules, limiting the potential for truly creative and responsive designs. We propose a novel approach leveraging reinforcement learning to explore the vast space of possible patterns, rewarding patterns that demonstrate emergent complexity and self-organization. The core mechanism centers on using a reinforcement learning agent to iteratively refine patterns based on feedback, leading to the discovery of aesthetically pleasing and functionally relevant geometric forms. This work aims to advance computational geometry by providing a method for automated pattern design, pushing the boundaries of what is possible with algorithmic creativity.
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
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This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.
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This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
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It is found that roles of MVPs in startups were not fully aware by entrepreneurs, and entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP).
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It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.
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A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
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A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
