Procesos proyectuales algorítmicos en estrategias de diseño no-lineales

El enorme avance que representó el descubrimiento de ecuaciones diferenciales por parte de Newton y Leibniz, sentó las bases definitivas de la ciencia clásica. Tiempo más tarde, la ciencia daría con el hecho de que el universo es un tejido complejo de estructuras no-lineales. El término no-lineal es utilizado para referirse a sistemas en donde la información de entrada, no tiene siempre un correlato de causa y efecto con la de salida. Los sistemas no-lineales tienden a ser impredecibles, indeterminados. La aplicación de este concepto al diseño, apoyado por el actual avance en materia de software paramétrico, parece ser el modo más indicado para abordar el actual paradigma de la complejidad

Generaciones digitales

Durante las últimas décadas, el desarrollo del uso de metodologías digitales para la generación de forma ha sido vertiginoso. Gran cantidad de oficinas de Arquitectura se han ido volcando al uso de estas nuevas y efervescentes técnicas y metodologías de producción proyectual, con el objeto de no ser dejados atrás por el creciente número de arquitectos jóvenes que, aprovechando el uso y las ventajas de éstas plataformas, han ido acaparando la escena principal de la Arquitectura contemporánea

Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks

We present a natural extension to E(n)-equivariant graph neural networks that uses multiple equivariant vectors per node. We formulate the extension and show that it improves performance across different physical systems benchmark tasks, with minimal differences in runtime or number of parameters. The proposed multichannel EGNN outperforms the standard singlechannel EGNN on N-body charged particle dynamics, molecular property predictions, and predicting the trajectories of solar system bodies. Given the additional benefits and minimal additional cost of multi-channel EGNN, we suggest that this extension may be of practical use to researchers working in machine learning for the physical science

PI-based controller for low-power distributed inverters to maximise reactive current injection while avoiding over voltage during voltage sags

This paper is a postprint of a paper submitted to and accepted for publication in IET Power Electronics and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.In the recently deregulated power system scenario, the growing number of distributed generation sources should be considered as an opportunity to improve stability and power quality along the grid. To make progress in this direction, this work proposes a reactive current injection control scheme for distributed inverters under voltage sags. During the sag, the inverter injects, at least, the minimum amount of reactive current required by the grid code. The flexible reactive power injection ensures that one phase current is maintained at its maximum rated value, providing maximum support to the most faulted phase voltage. In addition, active power curtailment occurs only to satisfy the grid code reactive current requirements. As well as, a voltage control loop is implemented to avoid overvoltage in non-faulty phases, which otherwise would probably occur due to the injection of reactive current into an inductive grid. The controller is proposed for low-power rating distributed inverters where conventional voltage support provided by large power plants is not available. The implementation of the controller provides a low computational burden because conventional PI-based control loops may apply. Selected experimental results are reported in order to validate the effectiveness of the proposed control scheme.Peer ReviewedPostprint (updated version

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset. Our toolkit provides: 1. A scalable machine learning workflow for materials science leveraging PyTorch Lightning, which enables seamless scaling across different computation capabilities (laptop, server, cluster) and hardware platforms (CPU, GPU, XPU). 2. Deep Graph Library (DGL) support for rapid graph neural network prototyping and development. By publishing and sharing this toolkit with the research community via open-source release, we hope to: 1. Lower the entry barrier for new machine learning researchers and practitioners that want to get started with the OpenCatalyst dataset, which presently comprises the largest computational materials science dataset. 2. Enable the scientific community to apply advanced machine learning tools to high-impact scientific challenges, such as modeling of materials behavior for clean energy applications. We demonstrate the capabilities of our framework by enabling three new equivariant neural network models for multiple OpenCatalyst tasks and arrive at promising results for compute scaling and model performance.Comment: Paper accompanying Open-Source Software from https://github.com/IntelLabs/matscim

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

Mitigating the climate crisis requires a rapid transition towards lower carbon energy. Catalyst materials play a crucial role in the electrochemical reactions involved in a great number of industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis. To reduce the amount of energy spent on such processes, we must quickly discover more efficient catalysts to drive the electrochemical reactions. Machine learning (ML) holds the potential to efficiently model the properties of materials from large amounts of data, and thus to accelerate electrocatalyst design. The Open Catalyst Project OC20 data set was constructed to that end. However, most existing ML models trained on OC20 are still neither scalable nor accurate enough for practical applications. Here, we propose several task-specific innovations, applicable to most architectures, which increase both computational efficiency and accuracy. In particular, we propose improvements in (1) the graph creation step, (2) atom representations and (3) the energy prediction head. We describe these contributions and evaluate them on several architectures, showing up to 5$\times$ reduction in inference time without sacrificing accuracy.Comment: Accepted at the NeurIPS 2022 AI for Accelerated Materials Design Worksho