Unverified50% confidenceFactExact time
PINNs use automatic differentiation to compute the various-order derivatives of network outputs with respect to inputs, enabling the solution of forward and inverse problems with almost no labeled data.
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9/9/2026
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UnverifiedThe core idea of PINN is to simultaneously include data fitting error and physical equation residuals in the neural network loss function, computing partial derivatives of outputs with respect to input coordinates via automatic differentiation.71% similarUnverified本项目用Minimax求解器对每个可达棋盘状态计算最佳落子,形成棋盘状态到最佳落子的映射作为神经网络监督学习标签,属于广义的知识蒸馏65% similarVerifiedAutomatic differentiation builds a computation graph by recording all tensor operations at runtime, then uses reverse-mode differentiation to automatically compute exact gradients of arbitrarily complex functions.63% similarUnverifiedAutomatic differentiation precisely applies the chain rule by decomposing a program into a sequence of elementary operations, avoiding both the rounding errors of numerical differentiation and the expression swell of symbolic differentiation.62% similarUnverified自动微分通过在前向计算过程中记录每一步操作及其局部导数规则构建计算树,反向传播时沿计算树应用链式法则计算梯度62% similar
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