arXiv:2607.06976v1 Announce Type: new Abstract: In this paper, we propose an efficient hybrid least squares/gradient descent (LSGD) method for MIONets to accelerate training. This method generalizes the LSGD method for DeepONets. Since MIONet is the sum of the entrywise product of multiple branch networks and a trunk network, it can be viewed as a multilinear function with respect to the last layer parameters of each branch network. These sets of parameters can be optimized using the alternating least squares method, where we solve the LS system for a single branch network in turn. To handle t
The paper leverages a renewed focus on efficient neural network training methods to build upon existing DeepONet improvements, adapting them for the more complex MIONet architecture.
This development proposes a methodology to accelerate the training of MIONets, potentially making these complex neural networks more practical for real-world scientific and engineering applications.
The proposed hybrid least squares/gradient descent method suggests an avenue for significantly faster MIONet convergence, reducing computational costs and time for model development and deployment.
- · AI researchers
- · Scientific computing
- · Engineering simulation
- · Cloud computing providers
- · Inefficient AI training methods
Faster training times for MIONet models enable quicker iteration and deployment in complex scientific and engineering domains.
Reduced computational resource needs for training could make advanced AI models more accessible to smaller research groups and organizations.
Accelerated development cycles for AI-driven simulations in fields like materials science or climate modeling could lead to unexpected breakthroughs.
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Read at arXiv cs.LG