The current PFN model may face limitations in scalability to higher-dimensional design spaces due to computational constraints and model complexity. To address these limitations, the following strategies can be implemented:
Model Architecture Optimization: Optimizing the architecture of the PFN model to handle higher-dimensional data more efficiently can improve scalability. This may involve adjusting the number of layers, neurons, or attention mechanisms to better capture complex data dependencies.
Data Preprocessing: Preprocessing the input data to reduce dimensionality or extract relevant features can help mitigate the challenges of higher-dimensional design spaces. Techniques like dimensionality reduction or feature selection can be applied to streamline the input data.
Parallelization and Distributed Computing: Leveraging parallel computing and distributed systems can enhance the scalability of the PFN model to higher-dimensional spaces. By distributing the computational workload across multiple processors or GPUs, the model can handle larger datasets and more complex problems.
Transfer Learning and Fine-Tuning: Utilizing transfer learning techniques to pre-train the PFN model on related tasks or datasets and fine-tuning it for specific higher-dimensional design spaces can improve scalability and performance.
By implementing these strategies, the PFN model can overcome its limitations in scalability to higher-dimensional design spaces and effectively handle complex engineering optimization problems.