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Tag: DeepONet

Solving Inverse Problems With Physics-Informed DeepONet: A Practical Guide With Code Implementation

In my previous blog, we delved into the concept of physics-informed DeepONet (PI-DeepONet) and explored why it is particularly suitable for operator learning, i.e., learning mappings from an input function to an output function. We also turned theory into code and implemented a PI-DeepONet that...

Operator Learning via Physics-Informed DeepONet: Let’s Implement It From Scratch

Ordinary and partial differential equations (ODEs/PDEs) are the backbone of many disciplines in science and engineering, from physics and biology to economics and climate science. They are fundamental tools used to describe physical systems and processes, capturing the continuous change of quantitie...

Operator Learning via Physics-Informed DeepONet: Let’s Implement It From Scratch

Ordinary and partial differential equations (ODEs/PDEs) are the backbone of many disciplines in science and engineering, from physics and biology to economics and climate science. They are fundamental tools used to describe physical systems and processes, capturing the continuous change of quantitie...