Precision agriculture promises higher yields and sustainability, but adoption is slowed by the high cost of cyber-physical systems (CPS) and the lack of systematic design methods. We present a cost-aware design space exploration (DSE) framework for multimodal drone-rover platforms to integrate budget, energy, sensing, payload, computation, and communication constraints. Using integer linear programming (ILP) with SAT-based verification, our approach trades off among cost, coverage, and payload while ensuring constraint compliance and a multitude of alternatives. We conduct case studies on smaller and larger-sized farms to show that our method consistently achieves full coverage within budget while maximizing payload efficiency, outperforming state-of-the-art CPS DSE approaches.
Bharathi Kannan Nithyanantham, Tobias Sesterhenn, Ashwin Nedungadi
et al.
Bringing generative AI into the architecture, engineering and construction (AEC) field requires systems that can translate natural language instructions into actions on standardized data models. We present MCP4IFC, a comprehensive open-source framework that enables Large Language Models (LLMs) to directly manipulate Industry Foundation Classes (IFC) data through the Model Context Protocol (MCP). The framework provides a set of BIM tools, including scene querying tools for information retrieval, predefined functions for creating and modifying common building elements, and a dynamic code-generation system that combines in-context learning with retrieval-augmented generation (RAG) to handle tasks beyond the predefined toolset. Experiments demonstrate that an LLM using our framework can successfully perform complex tasks, from building a simple house to querying and editing existing IFC data. Our framework is released as open-source to encourage research in LLM-driven BIM design and provide a foundation for AI-assisted modeling workflows. Our code is available at https://show2instruct.github.io/mcp4ifc/.
Lou Schwartz, Mohammad Ghoniem, Valérie Maquil
et al.
Wall-Sized Displays have spatial characteristics that are difficult to address during user interface design. The design at scale 1:1 could be part of the solution. In this paper, we present the results of two user studies and one technology review, exploring the usability of popular, desktop-optimized prototyping tools, for designing at scale on Wall-Sized Displays. We considered two wall-sized display setups, and three different interaction methods: touch, a keyboard equipped with a touchpad, and a tablet. We observed that designing at scale 1:1 was appreciated. Tablet-based interaction proved to be the most comfortable interaction method, and a mix of interaction modalities is promising. In addition, care must be given to the surrounding environment, such as furniture. We propose twelve design guidelines for a design tool dedicated to this specific context. Overall, existing user interface design tools do not yet fully support design on and for wall-sized displays and require further considerations in terms of placement of user interface elements and the provision of additional features.
Accurate building energy forecasting is essential, yet traditional heuristics often lack precision, while advanced models can be opaque and struggle with generalization by neglecting physical principles. This paper introduces BuildEvo, a novel framework that uses Large Language Models (LLMs) to automatically design effective and interpretable energy prediction heuristics. Within an evolutionary process, BuildEvo guides LLMs to construct and enhance heuristics by systematically incorporating physical insights from building characteristics and operational data (e.g., from the Building Data Genome Project 2). Evaluations show BuildEvo achieves state-of-the-art performance on benchmarks, offering improved generalization and transparent prediction logic. This work advances the automated design of robust, physically grounded heuristics, promoting trustworthy models for complex energy systems.
Yigitcan Comlek, Thang Duc Pham, Randall Snurr
et al.
Data-driven materials design often encounters challenges where systems require or possess qualitative (categorical) information. Metal-organic frameworks (MOFs) are an example of such material systems. The representation of MOFs through different building blocks makes it a challenge for designers to incorporate qualitative information into design optimization. Furthermore, the large number of potential building blocks leads to a combinatorial challenge, with millions of possible MOFs that could be explored through time consuming physics-based approaches. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently without any human intervention. Our approach provides three main advantages: (i) no specific physical descriptors are required and only building blocks that construct the MOFs are used in global optimization through qualitative representations, (ii) the method is application and property independent, and (iii) the latent variable approach provides an interpretable model of qualitative building blocks with physical justification. To demonstrate the effectiveness of our method, we considered a design space with more than 47,000 MOF candidates. By searching only ~1% of the design space, LVGP-MOBBO was able to identify all MOFs on the Pareto front and more than 97% of the 50 top-performing designs for the CO$_2$ working capacity and CO$_2$/N$_2$ selectivity properties. Finally, we compared our approach with the Random Forest algorithm and demonstrated its efficiency, interpretability, and robustness.
Thermal issue is a major concern in 3D integrated circuit (IC) design. Thermal optimization of 3D IC often requires massive expensive PDE simulations. Neural network-based thermal prediction models can perform real-time prediction for many unseen new designs. However, existing works either solve 2D temperature fields only or do not generalize well to new designs with unseen design configurations (e.g., heat sources and boundary conditions). In this paper, for the first time, we propose DeepOHeat, a physics-aware operator learning framework to predict the temperature field of a family of heat equations with multiple parametric or non-parametric design configurations. This framework learns a functional map from the function space of multiple key PDE configurations (e.g., boundary conditions, power maps, heat transfer coefficients) to the function space of the corresponding solution (i.e., temperature fields), enabling fast thermal analysis and optimization by changing key design configurations (rather than just some parameters). We test DeepOHeat on some industrial design cases and compare it against Celsius 3D from Cadence Design Systems. Our results show that, for the unseen testing cases, a well-trained DeepOHeat can produce accurate results with $1000\times$ to $300000\times$ speedup.
The purpose of this study is to introduce ANN-based software for the fast evaluation of rotordynamics in the context of robust and integrated design. It is based on a surrogate model made of ensembles of artificial neural networks running in a Bokeh web application. The use of a surrogate model has sped up the computation by three orders of magnitude compared to the current models. ARRID offers fast performance information, including the effect of manufacturing deviations. As such, it helps the designer to make optimal design choices early in the design process. The designer can manipulate the parameters of the design and the operating conditions to obtain performance information in a matter of seconds.
Dominik Sisejkovic, Luca Collini, Benjamin Tan
et al.
Various logic-locking schemes have been proposed to protect hardware from intellectual property piracy and malicious design modifications. Since traditional locking techniques are applied on the gate-level netlist after logic synthesis, they have no semantic knowledge of the design function. Data-driven, machine-learning (ML) attacks can uncover the design flaws within gate-level locking. Recent proposals on register-transfer level (RTL) locking have access to semantic hardware information. We investigate the resilience of ASSURE, a state-of-the-art RTL locking method, against ML attacks. We used the lessons learned to derive two ML-resilient RTL locking schemes built to reinforce ASSURE locking. We developed ML-driven security metrics to evaluate the schemes against an RTL adaptation of the state-of-the-art, ML-based SnapShot attack.
The reduction of greenhouse gases from buildings forms the cornerstone of policy to mitigate the effects of climate change. However, the automation of urban scale building energy modeling systems required to meet global urban demand has proven challenging due to the bespoke characteristics of each city. One such point of uniqueness between cities is that of urban microclimate, which may play a major role in altering the performance of energy efficiency in buildings. This research proposes a way to rapidly collect urban microclimate data through the utilization of satellite readings and climate reanalysis. We then demonstrate the potential utility of this data by composing an analysis against three years of monthly building energy consumption data from New York City. As a whole, microclimate in New York City may be responsible for large swings in urban energy consumption. We estimate that Central Park may reduce the electricity consumption of adjacent buildings by 5-10%, while vegetation overall seems to have no appreciable impact on gas consumption. We find that favorable urban microclimates may decrease the gas consumption of some buildings in New York by 71% while others may increase gas consumption by as much as 221%. Additionally, microclimates may be responsible for the decrease of electricity consumption by 28.6% in regions or increases of 77% consumption in others. This work provides a method of curating global, high resolution microclimate data, allowing researchers to explore the invisible walls of urban microclimate which interact with the buildings around them.
Enrico Fatighenti, Michał Kapustka, Giovanni Mongardi
et al.
We consider generalized homogeneous roofs, i.e. quotients of simply connected, semisimple Lie groups by a parabolic subgroup, which admit two projective bundle structures. Given a general hyperplane section on such a variety, we consider the zero loci of its pushforwards along the projective bundle structures and we discuss their properties at the level of Hodge structures. In the case of the flag variety $F(1,2,n)$ with its projections to $\mathbb{P}^{n-1}$ and $G(2, n)$, we construct a derived embedding of the relevant zero loci by methods based on the study of $B$-brane categories in the context of a gauged linear sigma model.
We propose a machine learning based approach for automatic 3D building reconstruction and vectorization. Taking a single-channel photogrammetric digital surface model (DSM) and panchromatic (PAN) image as input, we first filter out non-building objects and refine the building shapes of input DSM with a conditional generative adversarial network (cGAN). The refined DSM and the input PAN image are then used through a semantic segmentation network to detect edges and corners of building roofs. Later, a set of vectorization algorithms are proposed to build roof polygons. Finally, the height information from the refined DSM is added to the polygons to obtain a fully vectorized level of detail (LoD)-2 building model. We verify the effectiveness of our method on large-scale satellite images, where we obtain state-of-the-art performance.
A digital finite impulse response (FIR) filter design is fully synthesizable, thanks to the mature CAD support of digital circuitry. On the contrary, analog mixed-signal (AMS) filter design is mostly a manual process, including architecture selection, schematic design, and layout. This work presents a systematic design methodology to automate AMS FIR filter design using a time approximation architecture without any tunable passive component, such as switched capacitor or resistor. It not only enhances the flexibility of the filter but also facilitates design automation with reduced analog complexity. The proposed design flow features a hybrid approximation scheme that automatically optimize the filter's impulse response in light of time quantization effects, which shows significant performance improvement with minimum designer's efforts in the loop. Additionally, a layout-aware regression model based on an artificial neural network (ANN), in combination with gradient-based search algorithm, is used to automate and expedite the filter design. With the proposed framework, we demonstrate rapid synthesis of AMS FIR filters in 65nm process from specification to layout.
Incompressible fluid flow around a cylinder is one of the classical problems in fluid-dynamics with strong relevance with many real-world engineering problems, for example, design of offshore structures or design of a pin-fin heat exchanger. Thus learning a high-accuracy surrogate for this problem can demonstrate the efficacy of a novel machine learning approach. In this work, we propose a physics-informed neural network (PINN) architecture for learning the relationship between simulation output and the underlying geometry and boundary conditions. In addition to using a physics-based regularization term, the proposed approach also exploits the underlying physics to learn a set of Fourier features, i.e. frequency and phase offset parameters, and then use them for predicting flow velocity and pressure over the spatio-temporal domain. We demonstrate this approach by predicting simulation results over out of range time interval and for novel design conditions. Our results show that incorporation of Fourier features improves the generalization performance over both temporal domain and design space.
We present a fully-integrated sensing and control system which enables mobile manipulator robots to execute building tasks with millimeter-scale accuracy on building construction sites. The approach leverages multi-modal sensing capabilities for state estimation, tight integration with digital building models, and integrated trajectory planning and whole-body motion control. A novel method for high-accuracy localization updates relative to the known building structure is proposed. The approach is implemented on a real platform and tested under realistic construction conditions. We show that the system can achieve sub-cm end-effector positioning accuracy during fully autonomous operation using solely on-board sensing.
Objective: Main purpose of this study is to identify the ruderal plant species which spontaneously grows on the wall and roof surfaces in urban and sub-urban areas due to their limited ecological needs and to contribute to the creating of the sustainable green areas in urban environments by understanding the parameters that ruderals depend on while they require little maintenance and irrigation support if not no. Material and Methods: The main material of this study is the ruderal plants which were collected from totally 60 walls and 36 roof surfaces within six districts of Trabzon city –Akçaabat, Arsin, Çaykara, Of, Ortahisar and Yomra in Turkey. From these 96 habitats, 1540 plants samples form the walls and 448 plant samples from the roofs were collected. All the plant samples collected from the research area were identified in the herbarium of the faculty of forestry in Karadeniz Technical University. Apart from this, parametres affecting coverage rate of common species on three different habitats were analysed.Results: It was found that 448 samples from the roof surfaces distributed into 61 species while 1540 samples from the walls distributed into 196 species. Plus, according to the analyses, 28 species were found on all three different habitats. As a result of the observations, measurements and analyses, it is clear from the study that coverage rate of the plant species depends on anthropogenic interaction, daylight period and depth of the media but there is no relation with the number of the species on the surfaces.Conclusion: Ruderal plants are definitely important to study on, if the world wants the term sustainability to find its real meaning as they require nearly nothing to grow in hard conditions. In urban life, maintenance is getting more and more expensive for green areas in urban life and this makes it difficult for them to survive especially when cities have limited budget on this, which has often occurred all over the world recently. There is no doubt that ruderal plants offer a great opportunity for modern era urban areas with their limited needs to grow in hard conditions. Furthermore, when thinking about the fact a serious amount of the ruderal plants detected on all three basic habitats has a great landscape plant characteristics, the approaches to their usage in urban areas are really critical.
Probabilistic spin logic (PSL), based on networks of binary stochastic neurons (or p-bits), has been shown to provide a viable framework for many functionalities including Ising computing, Bayesian inference, invertible Boolean logic and image recognition. This paper presents a hardware building block for the PSL architecture, consisting of an embedded MTJ and a capacitive voltage adder of the type used in neuMOS. We use SPICE simulations to show how identical copies of these building blocks (or weighted p-bits) can be interconnected with wires to design and solve a small instance of the NP-complete Subset Sum Problem fully in hardware.