Computer Derives Natural Laws From Raw Data
ScienceDaily (Apr. 3, 2009) — If Isaac Newton had access to a supercomputer, he'd have had it watch apples fall – and let it figure out the physical matters. But the computer would have needed to run an algorithm, just developed by Cornell researchers, which can derive natural laws from observed data.
The researchers have taught a computer to find regularities in the natural world that become established laws – yet without any prior scientific knowledge on the part of the computer. They have tested their method, or algorithm, on simple mechanical systems and believe it could be applied to more complex systems ranging from biology to cosmology and be useful in analyzing the mountains of data generated by modern experiments that use electronic data collection.
The research is published in the journal Science (April 3, 2009) by Hod Lipson, Cornell associate professor of mechanical and aerospace engineering, and graduate student Michael Schmidt, a specialist in computational biology.
Their process begins by taking the derivatives of every variable observed with respect to every other – a mathematical way of measuring how one quantity changes as another changes. Then the computer creates equations at random using various constants and variables from the data. It tests these against the known derivatives, keeps the equations that come closest to predicting correctly, modifies them at random and tests again, repeating until it literally evolves a set of equations that accurately describe the behavior of the real system.
More on this at ScienceDaily.com.
The researchers have taught a computer to find regularities in the natural world that become established laws – yet without any prior scientific knowledge on the part of the computer. They have tested their method, or algorithm, on simple mechanical systems and believe it could be applied to more complex systems ranging from biology to cosmology and be useful in analyzing the mountains of data generated by modern experiments that use electronic data collection.
The research is published in the journal Science (April 3, 2009) by Hod Lipson, Cornell associate professor of mechanical and aerospace engineering, and graduate student Michael Schmidt, a specialist in computational biology.
Their process begins by taking the derivatives of every variable observed with respect to every other – a mathematical way of measuring how one quantity changes as another changes. Then the computer creates equations at random using various constants and variables from the data. It tests these against the known derivatives, keeps the equations that come closest to predicting correctly, modifies them at random and tests again, repeating until it literally evolves a set of equations that accurately describe the behavior of the real system.
More on this at ScienceDaily.com.
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