Artificial intelligence (AI) has revolutionized software development, shifting from task-specific codes (Software 1.0) to neural network-based approaches (Software 2.0). However, applying this transition in engineering software presents challenges, including low surrogate model accuracy, the curse of dimensionality in inverse design, and rising complexity in physical simulations. We introduce an interpolating neural network (INN), grounded in interpolation theory and tensor decomposition, to realize Engineering Software 2.0 by advancing data training, partial differential equation solving, and parameter calibration. INN offers orders of magnitude fewer trainable/solvable parameters for comparable model accuracy than traditional multi-layer perceptron (MLP) or physics-informed neural networks (PINN). Demonstrated in metal additive manufacturing, INN rapidly constructs an accurate surrogate model of Laser Powder Bed Fusion (L-PBF) heat transfer simulation, achieving sub-10-micrometer resolution for a 10 mm path in under 15 minutes on a single GPU. This makes a transformative step forward across all domains essential to engineering software.

翻译：人工智能（AI）已彻底革新软件开发，从任务特定代码（软件1.0）转向基于神经网络的方法（软件2.0）。然而，在工程软件中应用这一转变面临诸多挑战，包括代理模型精度不足、逆向设计中的维度灾难以及物理模拟复杂度的攀升。我们提出一种基于插值理论与张量分解的插值神经网络（INN），通过推进数据训练、偏微分方程求解和参数校准，实现工程软件2.0。与传统多层感知机（MLP）或物理信息神经网络（PINN）相比，INN在达到相当模型精度的前提下，所需可训练/可求解参数数量可降低数个数量级。在金属增材制造领域的演示表明，INN能快速构建激光粉末床熔融（L-PBF）传热模拟的高精度代理模型，在单GPU上15分钟内即可实现10毫米路径的亚10微米分辨率模拟。这为工程软件所有关键领域带来了变革性进步。