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AI surrogate with built-in physics speeds up low-power amplifier design

Researchers have developed an AI surrogate model that incorporates built-in physics to accelerate the design of low-power amplifiers. By embedding physical laws directly into the model, the approach reduces the need for slow, computationally heavy simulations, cutting design time significantly. The work highlights a growing trend of physics-informed machine learning being applied to electronics engineering, where it could shorten development cycles for energy-efficient amplifier circuits.

Why now: New research demonstrating physics-informed AI is drawing attention for its potential to speed up electronics design.

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