Cellular automata (CA) are powerful discrete models for complex spatiotemporal systems, yet estimating hidden parameters governing their dynamics remains challenging. We propose an attention-enhanced convolutional neural network (Attention-CNN) as a surrogate optimizer for identifying the jump parameter ( σ ) in a two-phase CA model. The jump parameter regulates neighborhood radius and cell mobility, shaping emergent spatial structures. The architecture combines convolutional layers for local feature extraction with a spatial-attention module that captures global dependencies, enabling adaptive focus on informative regions of the domain. Training data were generated across varying domain sizes ( 25× 25 – 150× 150 ) and CA iterations (0, 5, 25, and 50), yielding 80,000 labeled samples. The model achieved 90.53 × faster inference than AlexNet, demonstrating robustness and efficiency across resolutions. Attention heatmaps further reveal interpretable focus patterns aligned with CA dynamics. This framework provides a fast, data-driven surrogate for inverse parameter estimation in CA-based models.
This paper introduces a Gaussian Process method that predicts the outcome of a set of chemical reactions. These reactions are traditionally described in terms of Ordinary Differential Equations and modelled using numerical methods, which can be computationally expensive and time-consuming. Since the time-scales over which reactions occur can vary drastically within a single process, the resulting numerical systems are regularly very stiff, compounding the computation expense requirement. For many applications, especially those involving a large number of predictions, speeding up the calculations would be desirable. In this work, a Sparse Variational Gaussian Process has been trained on a relatively small amount of high-dimensional data to provide both predictions and the associated uncertainty. Results show that using this Gaussian Process on a commodity personal computer (PC) can provide estimates of species concentrations and thermodynamic properties significantly faster than a traditional solver, while remaining highly accurate and providing a statistically consistent quantification of that accuracy.
This position paper explores healthcare operations and logistics from a multicriteria decision-making (MCDM) perspective, emphasizing the integration of operations research (OR) models with healthcare information technology (HIT). It reviews key OR problem classes, including timetabling, facility location, resource allocation, inventory management, supply chains, and routing, and synthesizes the performance criteria and trade-offs among efficiency, cost, patient outcomes, equity, resilience, and sustainability. We argue that classical and modern MCDM techniques are shown to reveal and balance these trade-offs, guiding model formulation and governance in complex healthcare systems. Beyond methodological aspects, we call for two complementary integrations. The first is horizontal integration: interoperability-by-design across peer healthcare IT and logistics platforms (e.g., EHRs, laboratory and imaging systems, asset and inventory management, and supplier interfaces) so that OR models can reliably read and write executable data and actions. The second is vertical integration: alignment of decisions, uncertainty treatments, and information hand-offs across planning horizons, operationalized through a four-level hierarchy of real-time (H1), operational (H2), tactical (H3), and strategic (H4). This hierarchy aligns problems, decision methods, and information hand-offs across horizons, linking rapid response and scheduling at lower levels with long-term capacity planning and governance at higher levels. The paper concludes with a research agenda focused on (i) multiobjective modeling that elevates equity and sustainability as primary design goals, (ii) interoperability-by-design frameworks connecting OR tools with healthcare IT infrastructures, and (iii) time-scale-aware optimization combining robust, stochastic, and learning-augmented methods to achieve adaptive, reliable, and data-driven healthcare logistics.
Laminar Separation Bubbles (LSBs) often occur on airfoils at low-Reynolds numbers ( 10^4-10^5 ), involving laminar separation, transition, and turbulent reattachment that challenge traditional turbulence models. This work introduces a data-driven framework combining Field Inversion and Machine Learning (FIML) to enhance the SST κ -ω -γ model in LSB regimes. Field Inversion first infers a spatial corrective field β (x) that adjusts turbulent kinetic energy production to match time-averaged LES data, with a NURBS-based regularization ensuring smoothness and dimensionality reduction. Then, a DeepONet neural operator learns the functional dependence of β on local RANS variables and fluxes, enabling adaptive model correction within the solver. Applications to the SD7003 and Eppler 387 airfoils show accurate predictions of separation, reattachment, skin friction, and pressure recovery, comparable to those obtained by LES. The proposed approach advances physics-informed, data-driven turbulence closures for regimes where high-fidelity data are limited.
pythonOCC is a library that provides Python bindings for the Open CASCADE Technology (OCCT) C++ geometric modelling kernel. To integrate it into a gradient-based shape optimization, one requires to compute the so-called geometric sensitivities, e.g., derivatives of surface nodes with respect to the design parameters. To obtain this information, pythonOCC and OCCT were algorithmically differentiated. Here, they are modularly integrated in the form of a CAD plugin into a framework for multidisciplinary design analysis and optimization (MDAO) based on the DLR’s FlowSimulator HPC ecosystem. The CAD plugin allows a robust and metadata-enabled mesh-to-CAD association between MPI domain-decomposed mesh objects and the underlying CAD patches, as well as the computation of geometric sensitivities. The framework integration of pythonOCC is demonstrated in a context of gradient-based, aerodynamic shape optimization for a RAE2822 configuration in fully turbulent, transonic flow.
