🍩 Database of Original & Non-Theoretical Uses of Topology

(found 3 matches in 0.001135s)

  1. An Industry Case of Large-Scale Demand Forecasting of Hierarchical Components (2019)

    Rodrigo Rivera-Castro, Ivan Nazarov, Yuke Xiang, Ivan Maksimov, Aleksandr Pletnev, Evgeny Burnaev
    Abstract Demand forecasting of hierarchical components is essential in manufacturing. However, its discussion in the machine-learning literature has been limited, and judgemental forecasts remain pervasive in the industry. Demand planners require easy-to-understand tools capable of delivering state-of-the-art results. This work presents an industry case of demand forecasting at one of the largest manufacturers of electronics in the world. It seeks to support practitioners with five contributions: (1) A benchmark of fourteen demand forecast methods applied to a relevant data set, (2) A data transformation technique yielding comparable results with state of the art, (3) An alternative to ARIMA based on matrix factorization, (4) A model selection technique based on topological data analysis for time series and (5) A novel data set. Organizations seeking to up-skill existing personnel and increase forecast accuracy will find value in this work.

  2. Explainable Machine Learning Approach to Yield and Quality Improvements Using Deep Topological Data Analytics (2023)

    Janhavi Giri, Attila Lengyel
    Abstract Abstract. In wafer fabrication, data is collected and analyzed to prevent process deviations that could affect product quality and wafer yield. However, the high-dimensional, sparse, and imbalanced nature of the data poses significant challenges to yield and quality root cause analysis. Deep Topological Data Analysis (DTDA) is an unsupervised machine learning method that clusters and models the data in the form of geometric objects such as graphs and their higher-dimensional versions. This method reduces the multidimensional dataset to two-dimensional networks or graphs, where each node represents a cluster of samples with similar characteristics, and an edge represents the presence of overlapping characteristics between the connecting nodes. DTDA provides insights into the necessary data elements required to conduct accurate analysis and helps engineers identify the features contributing to yield and quality issues, enabling corrective actions. Moreover, the approach prevents the waste of engineering resources and mitigates the impact on final manufacturing cost.

  3. Manifold Learning for Coherent Design Interpolation Based on Geometrical and Topological Descriptors (2023)

    D. Muñoz, O. Allix, F. Chinesta, J. J. Ródenas, E. Nadal
    Abstract In the context of intellectual property in the manufacturing industry, know-how is referred to practical knowledge on how to accomplish a specific task. This know-how is often difficult to be synthesised in a set of rules or steps as it remains in the intuition and expertise of engineers, designers, and other professionals. Today, a new research line in this concern spot-up thanks to the explosion of Artificial Intelligence and Machine Learning algorithms and its alliance with Computational Mechanics and Optimisation tools. However, a key aspect with industrial design is the scarcity of available data, making it problematic to rely on deep-learning approaches. Assuming that the existing designs live in a manifold, in this paper, we propose a synergistic use of existing Machine Learning tools to infer a reduced manifold from the existing limited set of designs and, then, to use it to interpolate between the individuals, working as a generator basis, to create new and coherent designs. For this, a key aspect is to be able to properly interpolate in the reduced manifold, which requires a proper clustering of the individuals. From our experience, due to the scarcity of data, adding topological descriptors to geometrical ones considerably improves the quality of the clustering. Thus, a distance, mixing topology and geometry is proposed. This distance is used both, for the clustering and for the interpolation. For the interpolation, relying on optimal transport appear to be mandatory. Examples of growing complexity are proposed to illustrate the goodness of the method.