Ordinary Differential Equations and Applied Dynamics Commons^™

A Differential Equation–Based Epidemiological Model Of Post-Operative Chronic Pain In Scoliosis Patients With Data-Driven Analysis, Paige Zhu, Padmanabhan Seshaiyer

CODEE Journal

Chronic post-surgical pain (CPSP) is a common and often overlooked complication following surgical correction of idiopathic scoliosis, impacting long-term patient wellbeing despite improvements in surgical outcomes. This project introduces a novel epidemiological framework to model the progression of CPSP using a compartmental structure. By applying a coupled system of nonlinear differential equations, we simulate pain trajectories over time and assess the effectiveness of surgical interventions. The model is implemented for a single-cohort population and extended to a two-cohort design to compare outcomes between Posterior Spinal Fusion (PSIF) and Vertebral Body Tethering (VBT) procedures. Further stratification by patient age enables us …

Dynamical Behavior, Extinction And Persistence In A Stochastic Delayed Two-Strain Epidemic Model With A Generalized Crowley-Martin Incidence Rate, Amina Allali, Dounia Bentaleb, Saida Amine

Mathematical Modelling and Numerical Simulation with Applications

This paper develops and analyzes a novel delayed stochastic SIR epidemic model with two interacting strains and general incidence functions. By establishing the existence and uniqueness of a positive global solution, the well-posedness of the model under stochastic perturbations is ensured. Extinction occurs when the stochastic reproduction number falls below unity, as demonstrated through Itô calculus and martingale convergence theorems, while persistence is guaranteed under the Crowley–Martin incidence when it exceeds one. Numerical experiments based on the Positive Preserving Truncated Euler–Maruyama (PPTEM) scheme confirm the analytical predictions and highlight the influence of time delay and noise intensity on the long-term …

(R2147) Effect Of Mass Variation With Log-Logistic Distribution In Perturbed Interacting Cr3bp, Abdullah Abdullah, Majhar Ali, S. K. Sahdev

Applications and Applied Mathematics: An International Journal (AAM)

This paper investigates the motion of the infinitesimal body in the perturbed restricted three-body problem where the primary is heterogeneous in shape and secondary is with modified Newtonian potential. With the use of log-logistic distribution, space-time transformation and the above-said perturbations, we determine the equations of motion and quasi-Jacobian integral. Further, we numerically perform the locations of equilibrium points, their stability, regions of motion, periodic orbits and Poincaré surfaces of section.

(R2155) Mathematical Insights Into Cancer Cells Growth Stability And Chaos, Pardeep Kumar, Kashish Agarwal, Tripti Anand, Sarita Jha

Applications and Applied Mathematics: An International Journal (AAM)

In this research paper, we examined a recently proposed three-dimensional dynamical model of cancer cell growth that explicitly couples the populations of tumour cells, healthy host cells, and immune cells (Pardeep et al.). We aim to elucidate the model’s novel biological features relative to classical tumour–immune interaction models and to characterize its dynamical regimes. This model is governed by nonlinear differential equations featuring a quadratic tumour proliferation term and bilinear coupling terms for tumour–immune and tumour–host interactions. Then, we performed the rigorous analysis by solving the fixed-point equations and from the Jacobian matrices at the resulting equilibria to identify the …

(R2159) A Novel Extension Of Picard’S Method For Fractional Initial Value Problems With Convergence Analysis On Finite And Infinite Intervals, Jag Mohan, Anju Sood

Applications and Applied Mathematics: An International Journal (AAM)

In recent years, fractional differential equations have emerged as powerful tools for modeling phenomena with memory and hereditary effects, owing to their non-local characteristics. These equations excel in tackling intricate problems across physics, engineering, and other fields. As analytical solutions are often infeasible, numerical methods play a vital role in their practical application. In this study, we have generalized Picard’s method to address fractional differential initial value problems with Caputo derivative, establishing an existence and uniqueness theorem applicable to both finite and infinite intervals. To substantiate our findings, we provide an example with graphical evidence demonstrating the convergence of the …

From Cork To Coasting: A Multi-Stage Ode Model Of Water-Rocket Flight, Viktoria Savatorova, Patryk Kustra, Ethan Dyer, Connor Carlson, Aleksei Talonov

CODEE Journal

Water rockets provide an affordable and engaging context for exploring applications of differential equations. Motivated by outreach activities conducted with undergraduate students, we develop a four-stage mathematical model of vertical water-rocket flight that is suitable for use in an ODE or mathematical modeling course. The model includes the cork-release phase, water-thrust propulsion, air-thrust propulsion with compressible and potentially choked flow, and the final ballistic stage with quadratic drag. While retaining key physical features, the model can be formulated as a system of ordinary differential equations that can be integrated numerically using tools familiar to students. We compare model predictions with …

Pinnlab: An Interactive Dashboard For Teaching Data-Driven Parameter Estimation In Differential Equations Using Physics-Informed Neural Networks, Mohan J. Parthasarathy, Padmanabhan Seshaiyer

CODEE Journal

Undergraduate instruction in ordinary differential equations (ODEs) is typically organized around the forward problem: finding solution trajectories when the governing equation and its parameters are known. In scientific practice, however, inverse problems are often more relevant, requiring unknown parameters to be inferred from noisy observations while assessing whether a proposed model is consistent with the data. We introduce PINNLab, an open-source MATLAB dashboard designed to help undergraduate students explore inverse modeling through physics-informed neural networks (PINNs). PINNLab presents PINNs as a complementary data-driven framework that connects differential equations, optimization, empirical data, and scientific machine learning. The instructional sequence is organized …

A Professional Development Course On Data-Driven Dynamical Systems At A Primarily Undergraduate Institution: Part A - Scientific Content, Alessandro M. Selvitella, Jeffrey R. Anderson

CODEE Journal

In the age of data-driven decision making, ordinary differential equations (ODEs) remain a powerful and interpretable framework for modeling dynamic processes, especially when integrated with modern tools from statistical learning and data-driven dynamical systems. Yet, general undergraduate and graduate curricula do not typically address key opportunities in data-driven dynamical systems.

This first paper in a series focuses on the mathematical and methodological core of a professional development course first developed in the academic year 2025-2026 at a Primarily Undergraduate Institution, Purdue University Fort Wayne. The curriculum developed in this course emphasized how regression, regularization, and sparse identification can be used …

Modeling The Impact Of Treatment During An Ongoing Tuberculosis Outbreak, Bach Le, Anna Robicheaux, Jude Shive, Alana Wells, Erin N. Bodine

Spora: A Journal of Biomathematics

In response to a significant tuberculosis outbreak in Wyandotte and Johnson Counties, Kansas, this study presents a compartmental model of ordinary differential equations to evaluate the impact of standard antibiotic treatment. The model incorporates latent, active, and treated disease states. Parameter values were informed by epidemiological data and uncertain parameter value ranges were explored systematically through uncertainty analysis using constrained Latin hypercube sampling. Cumulative infections and deaths, and the basic reproduction number, were computed over a five-year simulation period. Sensitivity analyses using partial rank correlation coefficients identified symptomatic treatment rate and transmission rate as primary drivers of cumulative infections and …

Machine Learning For Modeling In An Elementary Differential Equations Class, Nathan Albin, Andrew G. Bennett, Abhinav Chand

CODEE Journal

Mixing machine learning with modeling is an area of increasing importance. This paper presents a lesson where students model a spring-mass system both using traditional analysis with linear damping and using machine learning to learn the damping from real data. The machine learning is implemented in a Jupyter notebook hosted on Google Colab, allowing students to train the neural network without requiring the students to carry out coding. Students get experience with how machine learning can fail, how it can work, and the time and data requirements for machine learning to succeed, and are asked to apply this knowledge to …

Accuracy Of Parameter Estimation For A Simple Gene Regulatory Network Model Is Sensitive To Network Motif, Number Of Parameters Estimated, And Magnitude And Direction Of Regulatory Relationships, Nikki C. Chun, Kam Dahlquist

Honors Thesis

A gene regulatory network (GRN) is a set of transcription factors that regulate the expression of genes encoding other transcription factors. The dynamics of a GRN explain how gene expression changes over time. GRNmap is a MATLAB software package that uses ordinary differential equations to model dynamics of small-scale GRNs. We used the program to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in three related literature-derived GRNs based on yeast cold shock microarray data previously collected in the Dahlquist Lab. We noticed large differences in estimated weight values when 1-2% of the expression values were …

Dynamic Homeostasis In Relaxation And Bursting Oscillations, Christopher J. Ryzowicz

Biology and Medicine Through Mathematics Conference

No abstract provided.

Modeling, Control Analysis, And Parameter Estimation Of Epidemic Dynamics Using The Unscented Kalman Filter, Muhammad Imran, Saira Batool, Brett Mckinney

Biology and Medicine Through Mathematics Conference

No abstract provided.

Dynamics Of A Two-Stage Epidemiological Model With Post-Infection Mortality And Transmission Heterogeneity, B Sagar

Biology and Medicine Through Mathematics Conference

No abstract provided.

Modeling The Effects Of Chronic Stress On Type 2 Diabetes, Kris Mae Pasia

Biology and Medicine Through Mathematics Conference

No abstract provided.

A Mechanistic Model Of Adhesion, Inflammation, Sleep, And Pain In Sickle Cell Patients, Milan Marsh, Rebecca Segal

Biology and Medicine Through Mathematics Conference

No abstract provided.

A Logic-Based Differential Equation Model Of Endothelial Cell Function, Ella Froedge, Scarlett Hamilton, Mitchel Colebank

Biology and Medicine Through Mathematics Conference

No abstract provided.

Gap Junction Architecture And Synchronization Clusters In The Thalamic Reticular Nuclei, Alex Norwood

Biology and Medicine Through Mathematics Conference

No abstract provided.

Dynamical Systems Modeling To Determine The Role Of Crosstalk In Shaping Stat Signaling Profiles, Laura F. Strube, Anamarie Martinez, Neha Cheemalavagu, Karsen Shoger, James Faeder, Rachel Gottschalk

Biology and Medicine Through Mathematics Conference

No abstract provided.

A Phylogeny Informed Mathematical Model Of Hpai H5n1 Transmission And Control In Multi Host Systems, Oluwatosin Babasola

Biology and Medicine Through Mathematics Conference

No abstract provided.