Domain Knowledge Injection Guidance for Predictive Maintenance - Afroze, Lameya; Merkelbach, Silke; von Enzberg, Sebastian; Dumitrescu, Roman (ML4CPS 2023)

Learning of discrete models of variational PDEs from data - Offen, Christian; Ober-Blöbaum, Sina (Chaos)
We show how to learn discrete field theories from observational data of fields on a space-time lattice. For this, we train a neural network model of a discrete Lagrangian density such that the discrete Euler--Lagrange equations are consistent with the given training data. We, thus, obtain a structure-preserving machine learning architecture. Lagrangian densities are not uniquely defined by the solutions of a field theory. We introduce a technique to derive regularisers for the training process which optimise numerical regularity of the discrete field theory. Minimisation of the regularisers guarantees that close to the training data the discrete field theory behaves robust and efficient when used in numerical simulations. Further, we show how to identify structurally simple solutions of the underlying continuous field theory such as travelling waves. This is possible even when travelling waves are not present in the training data. This is compared to data-driven model order reduction based approaches, which struggle to identify suitable latent spaces containing structurally simple solutions when these are not present in the training data. Ideas are demonstrated on examples based on the wave equation and the Schrödinger equation.

Association of dietary patterns with diabetes-related comorbidities varies among diabetes endotypes - Weber, Katharina S.; Schlesinger, Sabrina; Lang, Alexander; Straßburger, Klaus; Maalmi, Haifa; Zhu, Anna; Zaharia, Oana-Patricia; Strom, Alexander; Bönhof, Gidon J.; Goletzke, Janina; Trenkamp, Sandra; Wagner, Robert; et al. (Nutrition, Metabolism and Cardiovascular Diseases)
Keywords: Cardiology and Cardiovascular Medicine; Nutrition and Dietetics; Endocrinology; Diabetes and Metabolism; Medicine (miscellaneous)

Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning - Peitz, Sebastian; Stenner, Jan; Chidananda, Vikas; Wallscheid, Oliver; Brunton, Steven L.; Taira, Kunihiko (Physica D: Nonlinear Phenomena)
We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs). Exploiting translational equivariances, the high-dimensional distributed control problem can be transformed into a multi-agent control problem with many identical, uncoupled agents. Furthermore, using the fact that information is transported with finite velocity in many cases, the dimension of the agents’ environment can be drastically reduced using a convolution operation over the state space of the PDE, by which we effectively tackle the curse of dimensionality otherwise present in deep reinforcement learning. In this setting, the complexity can be flexibly adjusted via the kernel width or by using a stride greater than one (meaning that we do not place an actuator at each sensor location). Moreover, scaling from smaller to larger domains – or the transfer between different domains – becomes a straightforward task requiring little effort. We demonstrate the performance of the proposed framework using several PDE examples with increasing complexity, where stabilization is achieved by training a low-dimensional deep deterministic policy gradient agent using minimal computing resources.

Insights into the First Multi-Transition-Metal Containing Ruddlesden Popper-Type Cathode for all-solid-state Fluoride Ion Batteries - Vanita, Vanita; Waidha, Aamir Iqbal; Vasala, Sami; Puphal, Pascal; Schoch, Roland; Glatzel, Pieter; Bauer, Matthias; Clemens, Oliver (Journal of Materials Chemistry A)
Promising cathode materials for fluoride-ion batteries (FIBs) are 3d transition metal containing oxides with Ruddlesden-Popper-type structure. So far, multi-elemental compositions were not investigated, but could alternate electrochemical performance similar to what has been found for cathode materials for lithium-ion batteries. Within this study, we investigate RP type La2Ni0.75Co0.25O4.08 as an intercalation-based active cathode material for all-solid-state FIBs. We determine the structural changes of La2Ni0.75Co0.25O4.08 during fluoride intercalation / de-intercalation by ex-situ X-ray diffraction, which showed that F- insertion leads to transformation of the parent phase to three different phases. Changes in Ni and Co oxidation states and coordination environment were examined by X-ray absorption spectroscopy and magnetic measurements in order to understand the complex reaction behaviour of the phases in detail, showing that the two transition metals behave differently in the charging and discharging process. Under optimized operating conditions, a cycle life of 120 cycles at a critical cut-off capacity of 40 mAh g-1 against Pb/PbF2 was obtained, which is one of the highest observed for intercalation electrode materials in FIBs so far. The average Coulombic efficiencies ranged from 85% to 90%. Thus, La2Ni0.75Co0.25O4.08 could be a promising candidate for cycling-stable high-energy cathode materials for all-solid-state FIBs
Keywords: General Materials Science; Renewable Energy; Sustainability and the Environment; General Chemistry

Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories - Otto, Samuel E.; Peitz, Sebastian; Rowley, Clarence W. (SIAM Journal on Applied Dynamical Systems)
Data-driven models for nonlinear dynamical systems based on approximating the underlying Koopman operator or generator have proven to be successful tools for forecasting, feature learning, state estimation, and control. It has become well known that the Koopman generators for control-affine systems also have affine dependence on the input, leading to convenient finite-dimensional bilinear approximations of the dynamics. Yet there are still two main obstacles that limit the scope of current approaches for approximating the Koopman generators of systems with actuation. First, the performance of existing methods depends heavily on the choice of basis functions over which the Koopman generator is to be approximated; and there is currently no universal way to choose them for systems that are not measure preserving. Secondly, if we do not observe the full state, we may not gain access to a sufficiently rich collection of such functions to describe the dynamics. This is because the commonly used method of forming time-delayed observables fails when there is actuation. To remedy these issues, we write the dynamics of observables governed by the Koopman generator as a bilinear hidden Markov model, and determine the model parameters using the expectation-maximization (EM) algorithm. The E-step involves a standard Kalman filter and smoother, while the M-step resembles control-affine dynamic mode decomposition for the generator. We demonstrate the performance of this method on three examples, including recovery of a finite-dimensional Koopman-invariant subspace for an actuated system with a slow manifold; estimation of Koopman eigenfunctions for the unforced Duffing equation; and model-predictive control of a fluidic pinball system based only on noisy observations of lift and drag.

Ernährung und Alltagsbewältigung - Ein Spannungsfeld für Individuum, Haushalt und Gesellschaft - Buyken, Anette; Libuda, Lars (DGEwissen)

Differentially Private Natural Language Models: Recent Advances and Future Directions - Hu, Lijie; Habernal, Ivan; Shen, Lei; Wang, Di (Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024)

DP-NMT: Scalable Differentially Private Machine Translation - Igamberdiev, Timour; Vu, Doan Nam Long; Kuennecke, Felix; Yu, Zhuo; Holmer, Jannik; Habernal, Ivan (Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations)
Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees; however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package. Our goal is to provide a platform for researchers to advance the development of privacy-preserving NMT systems, keeping the specific details of the DP-SGD algorithm transparent and intuitive to implement. We run a set of experiments on datasets from both general and privacy-related domains to demonstrate our framework in use. We make our framework publicly available and welcome feedback from the community.

Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles - Muschalik, Maximilian; Fumagalli, Fabian; Hammer, Barbara; Huellermeier, Eyke (Proceedings of the AAAI Conference on Artificial Intelligence)
While shallow decision trees may be interpretable, larger ensemble models like gradient-boosted trees, which often set the state of the art in machine learning problems involving tabular data, still remain black box models. As a remedy, the Shapley value (SV) is a well-known concept in explainable artificial intelligence (XAI) research for quantifying additive feature attributions of predictions. The model-specific TreeSHAP methodology solves the exponential complexity for retrieving exact SVs from tree-based models. Expanding beyond individual feature attribution, Shapley interactions reveal the impact of intricate feature interactions of any order. In this work, we present TreeSHAP-IQ, an efficient method to compute any-order additive Shapley interactions for predictions of tree-based models. TreeSHAP-IQ is supported by a mathematical framework that exploits polynomial arithmetic to compute the interaction scores in a single recursive traversal of the tree, akin to Linear TreeSHAP. We apply TreeSHAP-IQ on state-of-the-art tree ensembles and explore interactions on well-established benchmark datasets.
Keywords: Explainable Artificial Intelligence