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Found 8 papers, 2 papers with code
Data Scoping: Effectively Learning the Evolution of Generic Transport PDEs
However, deep learning architectures are fundamentally incompatible with the simulation of these PDEs.
Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators
High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control.
Smoothing the Rough Edges: Evaluating Automatically Generated Multi-Lattice Transitions
Additive manufacturing is advantageous for producing lightweight components while addressing complex design requirements.
Conceptual Design Generation Using Large Language Models
Expert evaluations indicate that the LLM-generated solutions have higher average feasibility and usefulness while the crowdsourced solutions have more novelty.
Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree search
The ultimate goal for a design agent is the ability to learn generalizable design behavior in a problem space without having seen it before.
Design Strategy Network: A deep hierarchical framework to represent generative design strategies in complex action spaces
The hierarchical architecture decomposes every action decision into first predicting a preferred spatial region in the design space and then outputting a probability distribution over a set of possible actions from that region.
Goal-Directed Design Agents: Integrating Visual Imitation with One-Step Lookahead Optimization for Generative Design
The visual imitation network from DLAgents is composed of a convolutional encoder-decoder network, acting as a rough planning step that is agnostic to feedback.
Learning to design from humans: Imitating human designers through deep learning
Finally, the designs generated by a computational team of these agents are then compared to actual human data for teams solving a truss design problem.
