Mathematics · Science · Code
Huginn— thought
Mathematics, science and computing for people who have to make it work. Many of our technical books come with working code in Python, Julia or R; some set their methods out as explicit, step-by-step algorithms; and some are pure mathematics, carried by theorem and proof. Every one shows its reasoning in full and tests its claims, by computation or by argument, so the reader can do the work, not just follow it.
Filtering Equations, Density Methods, and Particle Approximations
Erik Lindevall
Comparing Risks in Insurance, Finance and Economics
Ilze Strautiņa
Spectral Methods, Markov Chains, Particles and Neural Solvers
Sanne Kooistra
Finite Volumes, Time Steps and Phase Space
Sanne Kooistra
Gas Storage, Swing Contracts and Hydro Reservoirs as Stochastic Control Problems
Torbjørn Kvalvik
Theory and Algorithms, from N-Player Games to the Master Equation
Nikolaos Vardakis
Self-Exciting Point Processes in Seismology, Epidemiology, Insurance and Finance, with Python
Callum Whitlow
Importance Sampling, Large Deviations and Schrödinger Bridges
Noa Ben-Dor
Free Boundaries, Variational Inequalities and Their Common Limit
Nikolaos Vardakis
A Method of Lines Approach for the NLSE and GPE with Python
Þráinn Eiríksson
Pseudodifferential Operators and Markovian Potential Theory
T. Zamrik
Yield Risk, Seasonal Curves, Basis, and Crush Spreads in R
Ioanna Stamatiou
Reserve Risk, Aggregate Losses, and Capital Allocation in R
Sigrid Norrgård
Estimands, Adaptive Designs, Bayesian Borrowing, and External Controls in R
Ingrid Voss
SDEs, Sensitivities, and the Verified Solver Stack
Petra Vuković
Mass Matrices, DAEs, and the Stiff Solver Stack
Petra Vuković
Solving Evolutionary Equations Where the Semigroup Cannot
T. Zamrik
Solving PDEs with the FEniCSx Stack, Checked by Manufactured Solutions
Alexios Zervos
PINNs in PyTorch and DeepXDE, Checked by Monte Carlo
Amnon Gershon
Adjoint Gradients and Reproducible Solvers in PyTorch
Lena Ostrowski
