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Trained DFNN and TFNN models from the paper: Suryasentana, S. K., Sheil, B. B., & Stuyts, B. (2024). Practical approach for data-efficient metamodeling and real-time modeling of monopiles using physics-informed multifidelity data fusion

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PIMFNN

Trained DFNN and TFNN models from the paper: Suryasentana, S. K., Sheil, B. B., & Stuyts, B. (2024). Practical approach for data-efficient metamodeling and real-time modeling of monopiles using physics-informed multifidelity data fusion. Journal of Geotechnical and Geoenvironmental Engineering, 150(8), 06024005.

To test the trained models, simply run 'run_model.py'

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Trained DFNN and TFNN models from the paper: Suryasentana, S. K., Sheil, B. B., & Stuyts, B. (2024). Practical approach for data-efficient metamodeling and real-time modeling of monopiles using physics-informed multifidelity data fusion

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