Data-driven discovery of dimensionless numbers and scaling laws from experimental measurements

Dimensionless numbers and scaling laws provide elegant insights into the characteristic properties of physical systems. Classical dimensional analysis and similitude theory fail to identify a set of unique dimensionless numbers for a highly-multivariable system with incomplete governing equations. In this study, we embed the principle of dimensional invariance into a two-level machine learning scheme to automatically discover dominant and unique dimensionless numbers and scaling laws from data. The proposed methodology, called dimensionless learning, can reduce high-dimensional parametric spaces into descriptions involving just a few physically-interpretable dimensionless parameters, which significantly simplifies the process design and optimization of the system. We demonstrate the algorithm by solving several challenging engineering problems with noisy experimental measurements (not synthetic data) collected from the literature. The examples include turbulent Rayleigh-Benard convection, vapor depression dynamics in laser melting of metals, and porosity formation in 3D printing. We also show that the proposed approach can identify dimensionally-homogeneous differential equations with minimal parameters by leveraging sparsity-promoting techniques

Could mergers become more sustainable? A study of the stock exchange mergers of NASDAQ and OMX

This study investigates whether the merger of NASDAQ and OMX could reduce the portfolio diversification possibilities for stock market investors and whether it is necessary to implement national policies and international treaties for the sustainable development of financial markets. Our study is very important because some players in the stock markets have not yet realized that stock exchanges, during the last decades, have moved from government-owned or mutually-owned organizations to private companies, and, with several mergers having occurred, the market is tending gradually to behave like a monopoly. From our analysis, we conclude that increased volatility and reduced diversification opportunities are the results of an increase in the long-run comovement between each pair of indices in Nordic and Baltic stock markets (Denmark, Sweden, Finland, Estonia, Latvia, and Lithuania) and NASDAQ after the merger. We also find that the merger tends to improve the error-correction mechanism for NASDAQ so that it Granger-causes OMX, but OMX loses predictive power on NASDAQ after the merger. We conclude that the merger of NASDAQ and OMX reduces the diversification possibilities for stock market investors and our findings provide evidence to support the argument that it is important to implement national policies and international treaties for the sustainable development of financial markets

Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission

Multi-node communication, which refers to the interaction among multiple devices, has attracted lots of attention in many Internet-of-Things (IoT) scenarios. However, its huge amounts of data flows and inflexibility for task extension have triggered the urgent requirement of communication-efficient distributed data transmission frameworks. In this paper, inspired by the great superiorities on bandwidth reduction and task adaptation of semantic communications, we propose a federated learning-based semantic communication (FLSC) framework for multi-task distributed image transmission with IoT devices. Federated learning enables the design of independent semantic communication link of each user while further improves the semantic extraction and task performance through global aggregation. Each link in FLSC is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator for coarse-to-fine semantic extraction and meaning translation according to specific tasks. In order to extend the FLSC into more realistic conditions, we design a channel state information-based multiple-input multiple-output transmission module to combat channel fading and noise. Simulation results show that the coarse semantic information can deal with a range of image-level tasks. Moreover, especially in low signal-to-noise ratio and channel bandwidth ratio regimes, FLSC evidently outperforms the traditional scheme, e.g. about 10 peak signal-to-noise ratio gain in the 3 dB channel condition.Comment: This paper has been accepted by IEEE Internet of Things Journa

Parameterized Unit Testing in the Open Source Wild

With recent advances in test generation research, powerful test generation tools are now at the fingertips of developers in software industry. For example, Microsoft Research Pex, a state-of-the-art tool based on dynamic symbolic execution, has been shipped as IntelliTest in Visual Studio 2015. For test inputs automatically generated by such tool, to supply test oracles (beyond just uncaught runtime exceptions or crashes), developers can write formal specifications such as code contracts in the form of preconditions, postconditions, and class invariants. However, just like writing other types of formal specifications, writing code contracts, especially postconditions, is challenging. In the past decade, parameterized unit testing has emerged as a promising alternative to specify program behaviors under test in the form of unit tests. Developers can write parameterized unit tests (PUTs), unit-test methods with parameters, in contrast to conventional unit tests, without parameters. PUTs have been popularly supported by various unit testing frameworks for .NET along with the recent JUnit framework. However, there exists no study to offer insights on how PUTs are written by developers in either proprietary or open source development practices, posing barriers for various stakeholders to bring PUTs to widely adopted practices in software industry. To fill this gap, in this paper, we present the first empirical study of parameterized unit testing conducted on open source projects. We study hundreds of parameterized unit tests that open source developers wrote for these open source projects. Our study findings provide valuable insights for various stakeholders such as current or prospective PUT writers (e.g., developers), PUT framework designers, test-generation tool vendors, testing researchers, and testing educators.Ope

JAX-FEM: A differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science

This paper introduces JAX-FEM, an open-source differentiable finite element method (FEM) library. Constructed on top of Google JAX, a rising machine learning library focusing on high-performance numerical computing, JAX-FEM is implemented with pure Python while scalable to efficiently solve problems with moderate to large sizes. For example, in a 3D tensile loading problem with 7.7 million degrees of freedom, JAX-FEM with GPU achieves around 10$\times$ acceleration compared to a commercial FEM code depending on platform. Beyond efficiently solving forward problems, JAX-FEM employs the automatic differentiation technique so that inverse problems are solved in a fully automatic manner without the need to manually derive sensitivities. Examples of 3D topology optimization of nonlinear materials are shown to achieve optimal compliance. Finally, JAX-FEM is an integrated platform for machine learning-aided computational mechanics. We show an example of data-driven multi-scale computations of a composite material where JAX-FEM provides an all-in-one solution from microscopic data generation and model training to macroscopic FE computations. The source code of the library and these examples are shared with the community to facilitate computational mechanics research

A Characteristic Study of Parameterized Unit Tests in .NET Open Source Projects

In the past decade, parameterized unit testing has emerged as a promising method to specify program behaviors under test in the form of unit tests. Developers can write parameterized unit tests (PUTs), unit-test methods with parameters, in contrast to conventional unit tests, without parameters. The use of PUTs can enable powerful test generation tools such as Pex to have strong test oracles to check against, beyond just uncaught runtime exceptions. In addition, PUTs have been popularly supported by various unit testing frameworks for .NET and the JUnit framework for Java. However, there exists no study to offer insights on how PUTs are written by developers in either proprietary or open source development practices, posing barriers for various stakeholders to bring PUTs to widely adopted practices in software industry. To fill this gap, we first present categorization results of the Microsoft MSDN Pex Forum posts (contributed primarily by industrial practitioners) related to PUTs. We then use the categorization results to guide the design of the first characteristic study of PUTs in .NET open source projects. We study hundreds of PUTs that open source developers wrote for these open source projects. Our study findings provide valuable insights for various stakeholders such as current or prospective PUT writers (e.g., developers), PUT framework designers, test-generation tool vendors, testing researchers, and testing educators

DiffComplete: Diffusion-based Generative 3D Shape Completion

We introduce a new diffusion-based approach for shape completion on 3D range scans. Compared with prior deterministic and probabilistic methods, we strike a balance between realism, multi-modality, and high fidelity. We propose DiffComplete by casting shape completion as a generative task conditioned on the incomplete shape. Our key designs are two-fold. First, we devise a hierarchical feature aggregation mechanism to inject conditional features in a spatially-consistent manner. So, we can capture both local details and broader contexts of the conditional inputs to control the shape completion. Second, we propose an occupancy-aware fusion strategy in our model to enable the completion of multiple partial shapes and introduce higher flexibility on the input conditions. DiffComplete sets a new SOTA performance (e.g., 40% decrease on l_1 error) on two large-scale 3D shape completion benchmarks. Our completed shapes not only have a realistic outlook compared with the deterministic methods but also exhibit high similarity to the ground truths compared with the probabilistic alternatives. Further, DiffComplete has strong generalizability on objects of entirely unseen classes for both synthetic and real data, eliminating the need for model re-training in various applications.Comment: Project Page: https://ruihangchu.com/diffcomplete.htm

Statistical Parameterized Physics-Based Machine Learning Digital Twin Models for Laser Powder Bed Fusion Process

A digital twin (DT) is a virtual representation of physical process, products and/or systems that requires a high-fidelity computational model for continuous update through the integration of sensor data and user input. In the context of laser powder bed fusion (LPBF) additive manufacturing, a digital twin of the manufacturing process can offer predictions for the produced parts, diagnostics for manufacturing defects, as well as control capabilities. This paper introduces a parameterized physics-based digital twin (PPB-DT) for the statistical predictions of LPBF metal additive manufacturing process. We accomplish this by creating a high-fidelity computational model that accurately represents the melt pool phenomena and subsequently calibrating and validating it through controlled experiments. In PPB-DT, a mechanistic reduced-order method-driven stochastic calibration process is introduced, which enables the statistical predictions of the melt pool geometries and the identification of defects such as lack-of-fusion porosity and surface roughness, specifically for diagnostic applications. Leveraging data derived from this physics-based model and experiments, we have trained a machine learning-based digital twin (PPB-ML-DT) model for predicting, monitoring, and controlling melt pool geometries. These proposed digital twin models can be employed for predictions, control, optimization, and quality assurance within the LPBF process, ultimately expediting product development and certification in LPBF-based metal additive manufacturing.Comment: arXiv admin note: text overlap with arXiv:2208.0290