Pavlos Protopapas

Variational boosting transforms PINNs from ill-conditioned monoliths into a series of stable, well-structured subproblems, enabling robust second-order optimization.

Only 2-4 principal components can capture 95% of the variance in the solution space of the Burgers equation, revealing a striking effective dimensional reduction.

Achieving accurate reconstruction of scalar potentials in the false vacuum regime reveals new insights into strongly coupled quantum systems through advanced machine learning techniques.

Machine learning resolves 20,000 ambiguous X-ray source matches, revealing the limitations of traditional spatial cross-matching methods.