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Symbolic neural networks, such as Kolmogorov-Arnold Networks (KAN), offer a promising approach for integrating prior knowledge with data-driven methods, making them valuable for addressing inverse problems in scientific and engineering domains. This study explores the application of KAN in building physics, focusing on predictive modeling, knowledge discovery, and continuous learning. Through four case studies, we demonstrate KAN's ability to rediscover fundamental equations, approximate complex formulas, and capture time-dependent dynamics in heat transfer. While there are challenges in extrapolation and interpretability, we highlight KAN's potential to combine advanced modeling methods for knowledge augmentation, which benefits energy efficiency, system optimization, and sustainability assessments beyond the personal knowledge constraints of the modelers. Additionally, we propose a model selection decision tree to guide practitioners in appropriate applications for building physics., Comment: 19 pages, 5 figures

In this study, we propose to adopt a novel framework, Knowledge-integrated Machine Learning, for advancing Proton Exchange Membrane Water Electrolysis (PEMWE) development. Given the significance of PEMWE in green hydrogen production and the inherent challenges in optimizing its performance, our framework aims to meld data-driven models with domain-specific insights systematically to address the domain challenges. We first identify the uncertainties originating from data acquisition conditions, data-driven model mechanisms, and domain expertise, highlighting their complementary characteristics in carrying information from different perspectives. Building upon this foundation, we showcase how to adeptly decompose knowledge and extract unique information to contribute to the data augmentation, modeling process, and knowledge discovery. We demonstrate a hierarchical three-level framework, termed the "Ladder of Knowledge-integrated Machine Learning", in the PEMWE context, applying it to three case studies within a context of cell degradation analysis to affirm its efficacy in interpolation, extrapolation, and information representation. This research lays the groundwork for more knowledge-informed enhancements in ML applications in engineering., Comment: 21 pages, 7 figures

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. We investigated the synergetic pattern between the data-driven methods, empirical domain knowledge, and first-principles simulations. We showed the potential risk of biased results when using data-driven models without causal analysis. Using a case study assessing the implication of several design solutions on the energy consumption of a building, we proved the necessity of causal analysis during the data-driven modeling process. We concluded that: (a) Data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Causal analysis results can be used as an aid to first-principles simulation design and parameter checking to avoid cognitive biases. We proved the benefits of causal analysis when applied to data-driven models in building engineering., Comment: 16 pages,6 figures

Despite the digitalization trend and data volume surge, first-principles models (also known as logic-driven, physics-based, rule-based, or knowledge-based models) and data-driven approaches have existed in parallel, mirroring the ongoing AI debate on symbolism versus connectionism. Research for process development to integrate both sides to transfer and utilize domain knowledge in the data-driven process is rare. This study emphasizes efforts and prevailing trends to integrate multidisciplinary domain professions into machine acknowledgeable, data-driven processes in a two-fold organization: examining information uncertainty sources in knowledge representation and exploring knowledge decomposition with a three-tier knowledge-integrated machine learning paradigm. This approach balances holist and reductionist perspectives in the engineering domain., Comment: 8 pages, 4 figures

We discuss recent advances towards matter-wave interference experiments with free beams of metallic and dielectric nanoparticles. They require a brilliant source, an efficient detection scheme and a coherent method to divide the de Broglie waves associated with these clusters: We describe an approach based on a magnetron sputtering source which ejects an intense cluster beam with a wide mass dispersion but a small velocity spread of 10%. The source is universal as it can be used with all conducting and many semiconducting or even insulating materials. Here we focus on metals and dielectrics with a low work function of the bulk and thus a low cluster ionization energy. This allows us to realize photoionization gratings as coherent matter-wave beam splitters and also to realize an efficient ionization detection scheme. These new methods are now combined in an upgraded Talbot-Lau interferometer with three 266 nm depletion gratings. We here describe the experimental boundary conditions and how to realize them in the lab. This next generation of near-field interferometers shall allow us to soon push the limits of matter-wave interference to masses up to 10 megadaltons., Comment: 10 pages, 5 figures

The development of current building energy system operation has benefited from: 1. Informational support from the optimal design through simulation or first-principles models; 2. System load and energy prediction through machine learning (ML). Through the literature review, we note that in current control strategies and optimization algorithms, most of them rely on receiving information from real-time feedback or using only predictive signals based on ML data fitting. They do not fully utilize dynamic building information. In other words, embedding dynamic prior knowledge from building system characteristics simultaneously for system control draws less attention. In this context, we propose an engineer-friendly control strategy framework. The framework is integrated with a feedforward loop that embedded a dynamic building environment with leading and lagging system information involved: The simulation combined with system characteristic information is imported to the ML predictive algorithms. ML generates step-ahead information by rolling-window feed-in of simulation output to minimize the errors of its forecasting predecessor in a loop and achieve an overall optimal. We tested it in a case for heating system control with typical control strategies, which shows our framework owns a further energy-saving potential of 15%., Comment: 6 pages, 7 figures. Accepted by PLEA2022, Presentation is avaiable at:https://github.com/chenxiachan/chenxiachan.github.io/blob/master/files/PLEA-Xia_1166_PG.pdf

The demand for a huge amount of data for machine learning (ML) applications is currently a bottleneck in an empirically dominated field. We propose a method to combine prior knowledge with data-driven methods to significantly reduce their data dependency. In this study, component-based machine learning (CBML) as the knowledge-encoded data-driven method is examined in the context of energy-efficient building engineering. It encodes the abstraction of building structural knowledge as semantic information in the model organization. We design a case experiment to understand the efficacy of knowledge-encoded ML in sparse data input (1% - 0.0125% sampling rate). The result reveals its three advanced features compared with pure ML methods: 1. Significant improvement in the robustness of ML to extremely small-size and inconsistent datasets; 2. Efficient data utilization from different entities' record collections; 3. Characteristics of accepting incomplete data with high interpretability and reduced training time. All these features provide a promising path to alleviating the deployment bottleneck of data-intensive methods and contribute to efficient real-world data usage. Moreover, four necessary prerequisites are summarized in this study that ensures the target scenario benefits by combining prior knowledge and ML generalization., Comment: 9 pages, 3 figures

This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 prevalent NN models and their variants across four brain-computer interface (BCI) paradigms, we gauged their information representation capability. Rooted in comprehensive literature review findings, we proposed EEGNeX, a novel, purely ConvNet-based architecture. We pitted it against both existing cutting-edge strategies and the Mother of All BCI Benchmarks (MOABB) involving 11 distinct EEG motor imagination (MI) classification tasks and revealed that EEGNeX surpasses other state-of-the-art methods. Notably, it shows up to 2.1%-8.5% improvement in the classification accuracy in different scenarios with statistical significance (p < 0.05) compared to its competitors. This study not only provides deeper insights into designing efficient NN models for EEG data but also lays groundwork for future explorations into the relationship between bioelectric brain signals and NN architectures. For the benefit of broader scientific collaboration, we have made all benchmark models, including EEGNeX, publicly available at (https://github.com/chenxiachan/EEGNeX)., Comment: 19 pages, 6 figures

The performance gap between predicted and actual energy consumption in the building domain remains an unsolved problem in practice. The gap exists differently in both current mainstream methods: the first-principles model and the machine learning (ML) model. Inspired by the concept of time-series decomposition to identify different uncertainties, we proposed a hybrid-model approach by combining both methods to minimize this gap: 1. Use the first-principles method as an encoding tool to convert the building static features and predictable patterns in time-series simulation results; 2. The ML method combines the results as extra inputs with historical records simultaneously, trains the model to capture the implicit performance difference, and aligns to calibrate the output. To extend this approach in practice, a new concept in the modeling process: Level-of-Information (LOI), is introduced to leverage the balance between the investment of simulation modeling detail and the accuracy boost. The approach is tested over a three-year period, with hourly measured energy load from an operating commercial building in Shanghai. The result presents a dominant accuracy enhancement: The hybrid-model shows higher accuracy in prediction with better interpretability; More important, it releases the practitioners from modeling workload and computational resources in refining simulation. In summary, the approach provides a nexus for integrating domain knowledge via building simulation with data-driven methods. This mindset applies to solving general engineering problems and leads to improved prediction accuracy. The result and source data are available at https://github.com/ResearchGroup-G/PerformanceGap-Hybrid-Approach., Comment: 18 pages, 11 figures; The result and source data are available at https://github.com/ResearchGroup-G/PerformanceGap-Hybrid-Approach

"What-if" questions are intuitively generated and commonly asked during the design process. Engineers and architects need to inherently conduct design decisions, progressing from one phase to another. They either use empirical domain experience, simulations, or data-driven methods to acquire consequential feedback. We take an example from an interdisciplinary domain of energy-efficient building design to argue that the current methods for decision support have limitations or deficiencies in four aspects: parametric independency identification, gaps in integrating knowledge-based and data-driven approaches, less explicit model interpretation, and ambiguous decision support boundaries. In this study, we first clarify the nature of dynamic experience in individuals and constant principal knowledge in design. Subsequently, we introduce causal inference into the domain. A four-step process is proposed to discover and analyze parametric dependencies in a mathematically rigorous and computationally efficient manner by identifying the causal diagram with interventions. The causal diagram provides a nexus for integrating domain knowledge with data-driven methods, providing interpretability and testability against the domain experience within the design space. Extracting causal structures from the data is close to the nature design reasoning process. As an illustration, we applied the properties of the proposed estimators through simulations. The paper concludes with a feasibility study demonstrating the proposed framework's realization., Comment: 20 pages, 10 figures

Data-driven models created by machine learning, gain in importance in all fields of design and engineering. They, have high potential to assist decision-makers in creating novel, artefacts with better performance and sustainability. However,, limited generalization and the black-box nature of these models, lead to limited explainability and reusability. To overcome this, situation, we propose a component-based approach to create, partial component models by machine learning (ML). This, component-based approach aligns deep learning with systems, engineering (SE). The key contribution of the component-based, method is that activations at interfaces between the components, are interpretable engineering quantities. In this way, the, hierarchical component system forms a deep neural network, (DNN) that a priori integrates information for engineering, explainability. The, approach adapts the model structure to engineering methods of, systems engineering and to domain knowledge. We examine the, performance of the approach by the field of energy-efficient, building design: First, we observed better generalization of the, component-based method by analyzing prediction accuracy, outside the training data. Especially for representative designs, different in structure, we observe a much higher accuracy, (R2 = 0.94) compared to conventional monolithic methods, (R2 = 0.71). Second, we illustrate explainability by exemplary, demonstrating how sensitivity information from SE and rules, from low-depth decision trees serve engineering. Third, we, evaluate explainability by qualitative and quantitative methods, demonstrating the matching of preliminary knowledge and data-driven, derived strategies and show correctness of activations at, component interfaces compared to white-box simulation results, (envelope components: R2 = 0.92..0.99; zones: R2 = 0.78..0.93)., Comment: 20 pages

Previous studies demonstrated that infrared absorption spectra of blood sera may help disease detection. For clinical translation of this approach, it is important to determine the molecular origin of disease-related spectral perturbations. To that end, we supplemented infrared spectroscopy with biochemical fractionation and proteomic profiling, which provide detailed information about blood serum composition. We built a model to describe serum absorption based on the concentrations of the highly-abundant proteins and applied this framework to lung cancer detection. We find that it is the levels of acute-phase proteins that change most in the presence of the disease and generate its infrared signature. These findings inform future clinical trials and establish a framework that could be applied to probing of any disease.

The de Broglie wave nature of matter is a paradigmatic example of fundamental quantum physics and enables precise measurements of forces, fundamental constants and even material properties. However, even though matter-wave interferometry is nowadays routinely realized in many laboratories, this feat has remained an outstanding challenge for the vast class of native polypeptides, the building blocks of life, which are ubiquitous in biology but fragile and difficult to handle. Here, we demonstrate the quantum wave nature of gramicidin, a natural antibiotic composed of 15 amino acids. Femtosecond laser desorption of a thin biomolecular film with intensities up to 1~TW/cm$^2$ transfers these molecules into a cold noble gas jet. Even though the peptide's de Broglie wavelength is as tiny as 350~fm, the molecular coherence is delocalized over more than 20 times the molecular size in our all-optical time-domain Talbot-Lau interferometer. We compare the observed interference fringes for two different interference orders with a model that includes both a rigorous treatment of the peptide's quantum wave nature as well as a quantum chemical assessment of its optical properties to distinguish our result from classical predictions. The successful realization of quantum optics with this polypeptide as a prototypical biomolecule paves the way for quantum-assisted molecule metrology and in particular the optical spectroscopy of a large class of biologically relevant molecules.

Laser beam profilometry is an important scientific task with well-established solutions for beams propagating in air. It has, however, remained an open challenge to measure beam profiles of high-power lasers in ultra-high vacuum and in tightly confined spaces. Here we present a novel scheme that uses a single multi-mode fiber to scatter light and guide it to a detector. The method competes well with commercial systems in position resolution, can reach through apertures smaller than $500\times 500$~$\mu$m$^2$ and is compatible with ultra-high vacuum conditions. The scheme is simple, compact, reliable and can withstand laser intensities beyond 2~MW/cm$^2$.

Extending the range of quantum interferometry to a wider class of composite nanoparticles requires new tools to diffract matter waves. Recently, pulsed photoionization light gratings have demonstrated their suitability for high mass matter-wave physics. Here we extend quantum interference experiments to a new class of particles by introducing photofragmentation beam splitters into time-domain matter-wave interferometry. Photofragmentation gratings can act on objects as different as van der Waals clusters and biomolecules which are thermally unstable and often resilient to single-photon ionization. We present data that demonstrate this coherent beam splitting mechanism with clusters of hexafluorobenzene and we show single-photon depletion gratings based both on fragmentation and ionization for clusters of vanillin.

Matter-wave interferometry with atoms and molecules has attracted a rapidly growing interest over the past two decades, both in demonstrations of fundamental quantum phenomena and in quantum-enhanced precision measurements. Such experiments exploit the non-classical superposition of two or more position and momentum states which are coherently split and rejoined to interfere. Here, we present the experimental realization of a universal near-field interferometer built from three short-pulse single-photon ionization gratings. We observe quantum interference of fast molecular clusters, with a composite de Broglie wavelength as small as 275 fm. Optical ionization gratings are largely independent of the specific internal level structure and are therefore universally applicable to different kinds of nanoparticles, ranging from atoms to clusters, molecules and nanospheres. The interferometer is sensitive to fringe shifts as small as a few nanometers and yet robust against velocity-dependent phase shifts, since the gratings exist only for nanoseconds and form an interferometer in the time domain., Comment: 7 pages, 4 figures

The wave-particle duality of massive objects is a cornerstone of quantum physics and a key property of many modern tools such as electron microscopy, neutron diffraction or atom interferometry. Here we report on the first experimental demonstration of quantum interference lithography with complex molecules. Molecular matter-wave interference patterns are deposited onto a reconstructed Si(111) 7x7 surface and imaged using scanning tunneling microscopy. Thereby both the particle and the quantum wave character of the molecules can be visualized in one and the same image. This new approach to nanolithography therefore also represents a sensitive new detection scheme for quantum interference experiments., Comment: 4 pages, 3 figures