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The human brain development experiences a complex evolving cortical folding from a smooth surface to a convoluted ensemble of folds. Computational modeling of brain development has played an essential role in better understanding the process of cortical folding, but still leaves many questions to be answered. A major challenge faced by computational models is how to create massive brain developmental simulations with affordable computational sources to complement neuroimaging data and provide reliable predictions for brain folding. In this study, we leveraged the power of machine learning in data augmentation and prediction to develop a machine-learning-based finite element surrogate model to expedite brain computational simulations, predict brain folding morphology, and explore the underlying folding mechanism. To do so, massive finite element method (FEM) mechanical models were run to simulate brain development using the predefined brain patch growth models with adjustable surface curvature. Then, a GAN-based machine learning model was trained and validated with these produced computational data to predict brain folding morphology given a predefined initial configuration. The results indicate that the machine learning models can predict the complex morphology of folding patterns, including 3-hinge gyral folds. The close agreement between the folding patterns observed in FEM results and those predicted by machine learning models validate the feasibility of the proposed approach, offering a promising avenue to predict the brain development with given fetal brain configurations.

Currently, bioresorbable electronic devices are predominantly fabricated by complex and expensive vacuum‐based integrated circuit (IC) processes. Here, a low‐cost manufacturing approach for bioresorbable conductors on bioresorbable polymer substrates by evaporation–condensation‐mediated laser printing and sintering of Zn nanoparticle is reported. Laser sintering of Zn nanoparticles has been technically difficult due to the surface oxide on nanoparticles. To circumvent the surface oxide, a novel approach is discovered to print and sinter Zn nanoparticle facilitated by evaporation–condensation in confined domains. The printing process can be performed on low‐temperature substrates in ambient environment allowing easy integration on a roll‐to‐roll platform for economical manufacturing of bioresorbable electronics. The fabricated Zn conductors show excellent electrical conductivity (≈1.124 × 10⁶S m⁻¹), mechanical durability, and water dissolvability. Successful demonstration of strain gauges confirms the potential application in various environmentally friendly sensors and circuits.